{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fd4f9d65",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import functools\n",
    "from sklearn.datasets import load_iris\n",
    "import numpy as np\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0f5ac2eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()\n",
    "y = list(iris.target)\n",
    "y_new = y\n",
    "for i in range(len(y)):\n",
    "    if y[i] == 0:\n",
    "        y_new[i] = [1,0,0]\n",
    "    elif y[i] == 1:\n",
    "        y_new[i] = [0,1,0]\n",
    "    else:\n",
    "        y_new[i] = [0,0,1]\n",
    "X = iris.data\n",
    "y = np.array(y_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80c46200",
   "metadata": {},
   "outputs": [
    {
     "ename": "InvalidParameterError",
     "evalue": "The 'test_size' parameter of train_test_split must be a float in the range (0.0, 1.0), an int in the range [1, inf) or None. Got 0.0 instead.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mInvalidParameterError\u001b[0m                     Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[3], line 21\u001b[0m\n\u001b[0;32m     18\u001b[0m y_one_hot[np\u001b[38;5;241m.\u001b[39marange(y\u001b[38;5;241m.\u001b[39msize), y] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m     20\u001b[0m \u001b[38;5;66;03m# 2. 拆分训练集和测试集\u001b[39;00m\n\u001b[1;32m---> 21\u001b[0m X_train, X_test, y_train, y_test \u001b[38;5;241m=\u001b[39m train_test_split(X, y_one_hot, test_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.0\u001b[39m, random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m)\n",
      "File \u001b[1;32mg:\\anaconda\\anconda2\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py:203\u001b[0m, in \u001b[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    200\u001b[0m to_ignore \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mself\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcls\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m    201\u001b[0m params \u001b[38;5;241m=\u001b[39m {k: v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m params\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m to_ignore}\n\u001b[1;32m--> 203\u001b[0m validate_parameter_constraints(\n\u001b[0;32m    204\u001b[0m     parameter_constraints, params, caller_name\u001b[38;5;241m=\u001b[39mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\n\u001b[0;32m    205\u001b[0m )\n\u001b[0;32m    207\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    208\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m    209\u001b[0m         skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m    210\u001b[0m             prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m    211\u001b[0m         )\n\u001b[0;32m    212\u001b[0m     ):\n",
      "File \u001b[1;32mg:\\anaconda\\anconda2\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py:95\u001b[0m, in \u001b[0;36mvalidate_parameter_constraints\u001b[1;34m(parameter_constraints, params, caller_name)\u001b[0m\n\u001b[0;32m     89\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     90\u001b[0m     constraints_str \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m     91\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin([\u001b[38;5;28mstr\u001b[39m(c)\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mfor\u001b[39;00m\u001b[38;5;250m \u001b[39mc\u001b[38;5;250m \u001b[39m\u001b[38;5;129;01min\u001b[39;00m\u001b[38;5;250m \u001b[39mconstraints[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m or\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     92\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconstraints[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     93\u001b[0m     )\n\u001b[1;32m---> 95\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidParameterError(\n\u001b[0;32m     96\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparam_name\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m parameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcaller_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     97\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconstraints_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. Got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparam_val\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     98\u001b[0m )\n",
      "\u001b[1;31mInvalidParameterError\u001b[0m: The 'test_size' parameter of train_test_split must be a float in the range (0.0, 1.0), an int in the range [1, inf) or None. Got 0.0 instead."
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import copy\n",
    "# 1. 加载数据并预处理\n",
    "# 加载鸢尾花数据集\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "\n",
    "# 标准化数据\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(X)\n",
    "\n",
    "# 目标进行One-hot编码\n",
    "y_one_hot = np.zeros((y.size, y.max() + 1))\n",
    "y_one_hot[np.arange(y.size), y] = 1\n",
    "\n",
    "# 2. 拆分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = X,y_one_hot,np.array([]),np.array([])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ca9b3869",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "#计时器装饰器\n",
    "def time_it(func):\n",
    "    def wrapper(*args, **kwargs):\n",
    "        start_time = time.time()  # 记录开始时间\n",
    "        result = func(*args, **kwargs)  # 调用实际的函数\n",
    "        end_time = time.time()  # 记录结束时间\n",
    "        print(f\"Function '{func.__name__}' took {end_time - start_time:.4f} seconds to execute.\")\n",
    "        return result\n",
    "    return wrapper\n",
    "def process_lists(input_list):\n",
    "    # 将输入列表转换为numpy数组，以便进行高效的处理\n",
    "    arr = np.array(input_list)\n",
    "    \n",
    "    # 计算最小值、平均值、最大值\n",
    "    min_vals = np.min(arr, axis=0)  # 每列的最小值\n",
    "    avg_vals = np.mean(arr, axis=0)  # 每列的平均值\n",
    "    max_vals = np.max(arr, axis=0)  # 每列的最大值\n",
    "    \n",
    "    # 返回一个包含3个列表的列表\n",
    "    return [min_vals.tolist(), avg_vals.tolist(), max_vals.tolist()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "98ba7403",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义激活函数和它们的导数\n",
    "class Sigmoid:\n",
    "    @staticmethod\n",
    "    def activate(x):\n",
    "        return 1 / (1 + np.exp(-x))\n",
    "\n",
    "    @staticmethod\n",
    "    def derivative(x):\n",
    "        return x * (1 - x)\n",
    "\n",
    "class Softmax:\n",
    "    @staticmethod\n",
    "    def activate(x):\n",
    "        exp_values = np.exp(x - np.max(x, axis=-1, keepdims=True))  # 稳定化计算，避免溢出\n",
    "        return exp_values / np.sum(exp_values, axis=-1, keepdims=True)\n",
    "\n",
    "    @staticmethod\n",
    "    def derivative(x):\n",
    "        s = x.reshape(-1, 1)\n",
    "        return np.diagflat(s) - np.dot(s, s.T)\n",
    "\n",
    "#定义优化器\n",
    "class optimizer:\n",
    "    def update_parameters(self,params,gradients):\n",
    "        pass\n",
    "\n",
    "#梯度下降优化器\n",
    "class GradientDescentOptimizer(optimizer):\n",
    "    def __init__(self,learning_rate=0.01):\n",
    "        self.learning_rate = learning_rate\n",
    "\n",
    "    def update_parameters(self,params,gradients):\n",
    "        updated_params = []\n",
    "        for param,gradient in zip(params,gradients):\n",
    "            updated_params.append(param - self.learning_rate * gradient)\n",
    "        return updated_params\n",
    "\n",
    "#Adam优化器\n",
    "class AdamOptimizer(optimizer):\n",
    "    def __init__(self, learing_rate, beta1=0.9, beta2=0.999, epsilon=1e-8):\n",
    "        \"\"\"\n",
    "        Adam优化器初始化\n",
    "\n",
    "        参数：\n",
    "        - params: 待优化的参数，通常是模型中的权重和偏置\n",
    "        - learning_rate: 学习率\n",
    "        - beta1: 一阶矩估计的衰减率，通常设为0.9\n",
    "        - beta2: 二阶矩估计的衰减率，通常设为0.999\n",
    "        - epsilon: 防止除零错误的常数，通常设为1e-8\n",
    "        \"\"\"\n",
    "        self.learning_rate = learing_rate\n",
    "        self.beta1 = beta1\n",
    "        self.beta2 = beta2\n",
    "        self.epsilon = epsilon\n",
    "        self.v = 0\n",
    "        self.t = 1\n",
    "        \n",
    "    def update_parameters(self,params,gradients):\n",
    "        updated_params = []\n",
    "        \n",
    "        v=0\n",
    "        #计算二阶矩动量\n",
    "        for param,gradient in zip(params,gradients):\n",
    "            v += np.sum(gradient**2)\n",
    "\n",
    "        #计算二阶矩动量加权，并重新更新\n",
    "        self.v = (1-self.beta2)*v+self.beta2*self.v\n",
    "        for i,(param,gradient) in enumerate(zip(params,gradients)):\n",
    "            #计算一阶矩动量\n",
    "            m = (1-self.beta1)*gradient + self.beta1*self.m[i]\n",
    "            #重新更新\n",
    "            self.m[i] = m\n",
    "            \n",
    "            #除以偏移量\n",
    "            m = m/(1-self.beta1**self.t)\n",
    "            v = self.v/(1-self.beta2**self.t)\n",
    "\n",
    "            #梯度更新\n",
    "            k = self.learning_rate  / (np.sqrt(v) + self.epsilon)\n",
    "            updated_params.append(param - k * m)\n",
    "        \n",
    "        self.t += 1\n",
    "\n",
    "        return updated_params\n",
    "    \n",
    "    def init_m(self,gradients):\n",
    "        self.m = [np.zeros_like(param) for param in gradients]\n",
    "\n",
    "class AFBMOptimizer(optimizer):\n",
    "    def __init__(self, learing_rate, beta1=0.9, beta2=0.999, epsilon=1e-8):\n",
    "        \"\"\"\n",
    "        Adam优化器初始化\n",
    "\n",
    "        参数：\n",
    "        - params: 待优化的参数，通常是模型中的权重和偏置\n",
    "        - learning_rate: 学习率\n",
    "        - beta1: 一阶矩估计的衰减率，通常设为0.9\n",
    "        - beta2: 二阶矩估计的衰减率，通常设为0.999\n",
    "        - epsilon: 防止除零错误的常数，通常设为1e-8\n",
    "        \"\"\"\n",
    "        self.learning_rate = learing_rate\n",
    "        self.beta1 = beta1\n",
    "        self.beta2 = beta2\n",
    "        self.epsilon = epsilon\n",
    "        self.v = 0\n",
    "        self.t = 1\n",
    "        \n",
    "    def update_parameters(self,params,gradients):\n",
    "        updated_params = []\n",
    "        \n",
    "        v=0\n",
    "        #计算二阶矩动量\n",
    "        for param,gradient in zip(params,gradients):\n",
    "            v += np.sum(gradient**2)\n",
    "\n",
    "        #计算二阶矩动量加权，并重新更新\n",
    "        self.v = (1-self.beta2)*v+self.beta2*self.v\n",
    "        for i,(param,gradient) in enumerate(zip(params,gradients)):\n",
    "            #计算一阶矩动量\n",
    "            m = (1-self.beta1)*gradient + self.beta1*self.m[i]\n",
    "            #重新更新\n",
    "            self.m[i] = m\n",
    "            \n",
    "            #除以偏移量\n",
    "            m = m/(1-self.beta1**self.t)\n",
    "            v = self.v/(1-self.beta2**self.t)\n",
    "\n",
    "            #梯度更新\n",
    "            k = self.learning_rate  / (np.sqrt(v) + self.epsilon)\n",
    "            updated_params.append(param - k * m)\n",
    "        \n",
    "        self.t += 1\n",
    "\n",
    "        return updated_params\n",
    "    \n",
    "    def init_m(self,gradients):\n",
    "        self.m = [np.zeros_like(param) for param in gradients]\n",
    "\n",
    "class FISTAOptimizer(optimizer):\n",
    "    def __init__(self, learing_rate):\n",
    "\n",
    "        self.learning_rate = learing_rate\n",
    "        self.t = 1\n",
    "        self.t_next = 1\n",
    "\n",
    "    def update_parameters(self,params,gradients):\n",
    "        updated_params = []\n",
    "\n",
    "        for i,(param,gradient) in enumerate(zip(params,gradients)):\n",
    "\n",
    "            updated_params.append(param-self.learning_rate*gradient)\n",
    "        \n",
    "\n",
    "        return updated_params\n",
    "    \n",
    "#class NesterovSpokoinyOptimizer(optimizer):\n",
    "#    def __init__(self,learning_rate=0.01):\n",
    "#        self.learning_rate = learning_rate\n",
    "#\n",
    "#    def update_parameters(self,params,gradients):\n",
    "#        updated_params = []\n",
    "#        for param,gradient in zip(params,gradients):\n",
    "#\n",
    "#            updated_params.append(param - self.learning_rate * gradient)\n",
    "#        return updated_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0039caf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 神经网络类\n",
    "class NeuralNetwork:\n",
    "    def __init__(self, input_size, hidden_size, output_size, optimizer,learning_rate=0.1):\n",
    "        # 网络的初始化\n",
    "        #np.random.seed(1)\n",
    "        self.input_size = input_size\n",
    "        self.hidden_size = hidden_size\n",
    "        self.output_size = output_size\n",
    "        self.learning_rate = learning_rate\n",
    "        self.optimizer = optimizer\n",
    "\n",
    "        # 初始化权重和偏置\n",
    "        self.weights_input_hidden = np.random.rand(input_size, hidden_size)  # 输入层到隐藏层的权重\n",
    "        self.weights_hidden_output = np.random.rand(hidden_size, output_size)  # 隐藏层到输出层的权重\n",
    "        self.bias_hidden = np.random.rand(hidden_size)  # 隐藏层偏置\n",
    "        self.bias_output = np.random.rand(output_size)  # 输出层偏置\n",
    "        \n",
    "        # 激活函数\n",
    "        self.activation = Sigmoid()\n",
    "        self.output_activation = Softmax()\n",
    "\n",
    "    def forward_propagation(self, inputs):\n",
    "\n",
    "        # 输入层到隐藏层的计算\n",
    "        hidden_layer_input = np.dot(inputs, self.weights_input_hidden) + self.bias_hidden\n",
    "        hidden_layer_output = self.activation.activate(hidden_layer_input)\n",
    "\n",
    "        # 隐藏层到输出层的计算\n",
    "        output_layer_input = np.dot(hidden_layer_output, self.weights_hidden_output) + self.bias_output\n",
    "        output_layer_output = self.output_activation.activate(output_layer_input)\n",
    "\n",
    "        return hidden_layer_output, output_layer_output\n",
    "\n",
    "    def __add__(self,other):\n",
    "        \n",
    "        temp_nn = copy.deepcopy(self)\n",
    "        temp_nn.weights_input_hidden = self.weights_input_hidden+other.weights_input_hidden  # 输入层到隐藏层的权重\n",
    "        temp_nn.weights_hidden_output = self.weights_hidden_output+other.weights_hidden_output   # 隐藏层到输出层的权重\n",
    "        temp_nn.bias_hidden = self.bias_hidden+other.bias_hidden  # 隐藏层偏置\n",
    "        temp_nn.bias_output = self.bias_output+other.bias_output  \n",
    "\n",
    "        return temp_nn\n",
    "    \n",
    "    def __mul__(self,other):\n",
    "\n",
    "        temp_nn = copy.deepcopy(self)\n",
    "        temp_nn.weights_input_hidden = self.weights_input_hidden*other # 输入层到隐藏层的权重\n",
    "        temp_nn.weights_hidden_output = self.weights_hidden_output*other   # 隐藏层到输出层的权重\n",
    "        temp_nn.bias_hidden = self.bias_hidden*other  # 隐藏层偏置\n",
    "        temp_nn.bias_output = self.bias_output*other\n",
    "\n",
    "        return temp_nn\n",
    "    \n",
    "    def backward_propagation(self, inputs, hidden_layer_output, output_layer_output, outputs):\n",
    "        # 计算输出层的误差\n",
    "        output_error = outputs - output_layer_output  # 均方误差的梯度\n",
    "        #print(output_error)\n",
    "        output = output_error  # 交叉熵的梯度与 Softmax 输出的导数在一块儿\n",
    "\n",
    "        # 计算隐藏层的误差\n",
    "        hidden_error = output.dot(self.weights_hidden_output.T)  # 输出层误差传播到隐藏层\n",
    "        hidden = hidden_error * self.activation.derivative(hidden_layer_output)\n",
    "\n",
    "        # 计算权重和偏置的梯度\n",
    "        weights_input_hidden_gradient = -np.dot(inputs.T, hidden)\n",
    "        weights_hidden_output_gradient = -np.dot(hidden_layer_output.T, output)\n",
    "        bias_hidden_gradient = -np.sum(hidden, axis=0)\n",
    "        bias_output_gradient = -np.sum(output, axis=0)\n",
    "\n",
    "        return [weights_input_hidden_gradient, weights_hidden_output_gradient, bias_hidden_gradient, bias_output_gradient]\n",
    "\n",
    "    def update_parameters(self, gradients, learning_rate=None):\n",
    "        params = [self.weights_input_hidden, self.weights_hidden_output, self.bias_hidden, self.bias_output]\n",
    "        updated_params = self.optimizer.update_parameters(params, gradients)\n",
    "        self.weights_input_hidden, self.weights_hidden_output, self.bias_hidden, self.bias_output = updated_params\n",
    "    \n",
    "    def predict(self, inputs):\n",
    "        # 预测函数\n",
    "        _, predicted_output = self.forward_propagation(inputs)\n",
    "        return predicted_output\n",
    "    \n",
    "    def train_option_decorator(func):\n",
    "        def wrapper(self,inputs,outputs,train_option,epochs):\n",
    "            if train_option == \"GD\":\n",
    "                return self.train_GD(inputs,outputs,epochs)\n",
    "            elif train_option == \"NSA\":\n",
    "                return self.train_NSA(inputs,outputs,epochs)\n",
    "            elif train_option == \"NSA_plus\":\n",
    "                return self.train_NSA_plus(inputs,outputs,epochs)\n",
    "            elif train_option == \"Adam\":\n",
    "                return self.train_Adam(inputs,outputs,epochs)\n",
    "            elif train_option == \"FISTA\":\n",
    "                return self.train_FISTA(inputs,outputs,epochs)\n",
    "        return wrapper\n",
    "    \n",
    "    @train_option_decorator\n",
    "    def train(self, inputs, outputs,train_option, epochs):\n",
    "        pass\n",
    "    \n",
    "    @time_it\n",
    "    def train_GD(self, inputs, outputs,epochs):\n",
    "\n",
    "        #记录训练过程中的损失\n",
    "        train_loss = []\n",
    "\n",
    "        # 训练网络\n",
    "        for epoch in range(epochs):\n",
    "\n",
    "            # 前向传播\n",
    "            hidden_layer_output, output_layer_output = self.forward_propagation(inputs)\n",
    "\n",
    "            # 反向传播\n",
    "            gradients = self.backward_propagation(inputs, hidden_layer_output, output_layer_output, outputs)\n",
    "\n",
    "            # 更新参数\n",
    "            self.update_parameters(gradients)\n",
    "\n",
    "            loss = np.mean(np.square(outputs - output_layer_output))  # 均方误差损失\n",
    "\n",
    "            # 每 1000 个epoch输出一次损失\n",
    "            #if epoch % 1000 == 0:\n",
    "            #    print(f\"Epoch {epoch}, Loss: {loss}\")\n",
    "                \n",
    "            train_loss.append(loss)\n",
    "        \n",
    "        return train_loss\n",
    "    \n",
    "    @time_it\n",
    "    def train_NSA(self, inputs, outputs, epochs):\n",
    "        \n",
    "        #记录训练过程中的损失\n",
    "        train_loss = []\n",
    "        nn_y = copy.deepcopy(self)\n",
    "        nn_x = copy.deepcopy(self)\n",
    "        nn_z = copy.deepcopy(self)\n",
    "        \n",
    "        # 训练网络\n",
    "        for epoch in range(epochs-1):\n",
    "            if epoch==0:\n",
    "                hidden_layer_output, output_layer_output = nn_y.forward_propagation(inputs)\n",
    "                loss = np.mean(np.square(outputs - output_layer_output))  # 均方误差损失\n",
    "                train_loss.append(loss)\n",
    "                \n",
    "            epoch = epoch+1\n",
    "            #alpha_t\n",
    "            alpha = 5/(epoch+5)\n",
    "            ###y = (1-alpha)*x+alpha*z\n",
    "            nn_y = nn_x*(1-alpha)+nn_z*alpha\n",
    "\n",
    "            ## x = y-eta*grad(y)\n",
    "            # 前向传播\n",
    "            hidden_layer_output, output_layer_output = nn_y.forward_propagation(inputs)\n",
    "            # 反向传播\n",
    "            y_gradients = nn_y.backward_propagation(inputs, hidden_layer_output, output_layer_output, outputs)\n",
    "\n",
    "            #将nn_y的参数赋值给nn_x\n",
    "            nn_x = copy.deepcopy(nn_y)\n",
    "            #更新nn_x参数\n",
    "            nn_x.update_parameters(y_gradients)\n",
    "            \n",
    "            ###z = z-eta/alpha*grad(y)\n",
    "            new_y_gradients = [item/alpha for item in y_gradients]\n",
    "            nn_z.update_parameters(new_y_gradients)\n",
    "            self = copy.deepcopy(nn_x)\n",
    "            # 前向传播计算当前损失\n",
    "            hidden_layer_output, output_layer_output = self.forward_propagation(inputs)\n",
    "            \n",
    "            loss = np.mean(np.square(outputs - output_layer_output))  # 均方误差损失\n",
    "\n",
    "            # 每 1000 个epoch输出一次损失\n",
    "            #print(f\"Epoch {epoch}, Loss: {loss}\")\n",
    "                \n",
    "            train_loss.append(loss)\n",
    "        return train_loss,self\n",
    "    \n",
    "    @time_it\n",
    "    def train_NSA_plus(self, inputs, outputs, epochs):\n",
    "        #记录训练过程中的损失\n",
    "        train_loss = []\n",
    "        nn_y = copy.deepcopy(self)\n",
    "        nn_x = copy.deepcopy(self)\n",
    "        nn_z = copy.deepcopy(self)\n",
    "        time_y = 0\n",
    "        time_back = 0\n",
    "        time_compare = 0\n",
    "        time_z = 0\n",
    "        # 训练网络\n",
    "        for epoch in range(epochs-1):\n",
    "            \n",
    "            if epoch==0:\n",
    "                hidden_layer_output, output_layer_output = nn_y.forward_propagation(inputs)\n",
    "                loss = np.mean(np.square(outputs - output_layer_output))  # 均方误差损失\n",
    "                train_loss.append(loss)\n",
    "            time1 = time.time()   \n",
    "            epoch = epoch+1\n",
    "\n",
    "            #alpha_t\n",
    "            alpha = 5/(epoch+5)\n",
    "\n",
    "            ###y = (1-alpha)*x+alpha*z\n",
    "            nn_y = nn_x*(1-alpha)+nn_z*alpha\n",
    "\n",
    "            time2 = time.time()\n",
    "            time_y += time2-time1\n",
    "            ## x1 = y-eta*grad(y)\n",
    "            # 前向传播\n",
    "            hidden_layer_output, output_layer_output = nn_y.forward_propagation(inputs)\n",
    "            # 反向传播\n",
    "            y_gradients = nn_y.backward_propagation(inputs, hidden_layer_output, output_layer_output, outputs)\n",
    "            \n",
    "            ## x2 = x-eta*grad(x)\n",
    "            # 前向传播\n",
    "            hidden_layer_output, output_layer_output = nn_x.forward_propagation(inputs)\n",
    "            # 反向传播\n",
    "            x_gradients = nn_x.backward_propagation(inputs, hidden_layer_output, output_layer_output, outputs)\n",
    "            \n",
    "            time3 = time.time()\n",
    "            \n",
    "            #梯度下降更新nn_y参数\n",
    "            nn_y.update_parameters(y_gradients)\n",
    "            \n",
    "            #梯度下降更新nn_y参数\n",
    "            nn_x.update_parameters(x_gradients)\n",
    "\n",
    "            time4 = time.time()\n",
    "            time_back += time4-time2\n",
    "            #前向传播比较损失\n",
    "            hidden_layer_output,output_layer_output_y = nn_y.forward_propagation(inputs)\n",
    "            loss_y = np.mean(np.square(outputs - output_layer_output_y))\n",
    "            hidden_layer_output,output_layer_output_x = nn_x.forward_propagation(inputs)\n",
    "            loss_x = np.mean(np.square(outputs - output_layer_output_x))\n",
    "            \n",
    "            #print(\"C\",loss_x,loss_y)\n",
    "            if loss_y<loss_x:\n",
    "                nn_x = copy.deepcopy(nn_y)\n",
    "            elif loss_x<=loss_y:\n",
    "                nn_x = copy.deepcopy(nn_x)\n",
    "            \n",
    "            time5 = time.time()\n",
    "            time_compare += time5-time4\n",
    "            ###z = z-eta/alpha*grad(y)\n",
    "            new_y_gradients = [item/alpha for item in y_gradients]\n",
    "            nn_z.update_parameters(new_y_gradients)\n",
    "            self = copy.deepcopy(nn_x)\n",
    "            \n",
    "            time6 = time.time()\n",
    "            time_z += time6-time5\n",
    "            # 前向传播计算当前损失\n",
    "            hidden_layer_output, output_layer_output = self.forward_propagation(inputs)\n",
    "            \n",
    "            loss = np.mean(np.square(outputs - output_layer_output))  # 均方误差损失\n",
    "                \n",
    "            train_loss.append(loss)\n",
    "        print(f\"y:{time_y},back:{time_back},compare:{time_compare},z:{time_z}\")\n",
    "        return train_loss,self\n",
    "    \n",
    "    @time_it\n",
    "    def train_Adam(self, inputs, outputs, epochs):\n",
    "        #记录训练过程中的损失\n",
    "        train_loss = []\n",
    "\n",
    "        # 前向传播\n",
    "        hidden_layer_output, output_layer_output = self.forward_propagation(inputs)\n",
    "        # 反向传播\n",
    "        gradients = self.backward_propagation(inputs, hidden_layer_output, output_layer_output, outputs)\n",
    "\n",
    "        #初始化梯度\n",
    "        self.optimizer.init_m(gradients)\n",
    "\n",
    "        # 训练网络\n",
    "        for epoch in range(epochs):\n",
    "\n",
    "            # 前向传播\n",
    "            hidden_layer_output, output_layer_output = self.forward_propagation(inputs)\n",
    "\n",
    "            # 反向传播\n",
    "            gradients = self.backward_propagation(inputs, hidden_layer_output, output_layer_output, outputs)\n",
    "\n",
    "            # 更新参数\n",
    "            self.update_parameters(gradients)\n",
    "\n",
    "            loss = np.mean(np.square(outputs - output_layer_output))  # 均方误差损失\n",
    "\n",
    "            train_loss.append(loss)\n",
    "        \n",
    "        return train_loss,self\n",
    "    \n",
    "    @time_it\n",
    "    def train_FISTA(self, inputs, outputs, epochs):\n",
    "        #记录训练过程中的损失\n",
    "        train_loss = []\n",
    "\n",
    "        t = 1\n",
    "        # 训练网络\n",
    "        for epoch in range(epochs):\n",
    "\n",
    "            nn_x_k = copy.deepcopy(self)\n",
    "\n",
    "            # 前向传播\n",
    "            hidden_layer_output, output_layer_output = self.forward_propagation(inputs)\n",
    "\n",
    "            # 反向传播\n",
    "            gradients = self.backward_propagation(inputs, hidden_layer_output, output_layer_output, outputs)\n",
    "\n",
    "            # 更新参数\n",
    "            self.update_parameters(gradients)\n",
    "\n",
    "            nn_x_k_plus_1 = copy.deepcopy(self)\n",
    "\n",
    "            momentum = nn_x_k_plus_1 + nn_x_k*(-1)\n",
    "\n",
    "            t_next = (1+math.sqrt(1+4*t**2))/2\n",
    "\n",
    "            self = nn_x_k_plus_1 + momentum*((t-1)/t_next)\n",
    "\n",
    "            t = t_next\n",
    "            \n",
    "            hidden_layer_output, output_layer_output = self.forward_propagation(inputs)\n",
    "\n",
    "            loss = np.mean(np.square(outputs - output_layer_output))  # 均方误差损失\n",
    "\n",
    "            train_loss.append(loss)\n",
    "        \n",
    "        return train_loss,self\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f9113f9f",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'numpy' has no attribute 'seed'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[20], line 9\u001b[0m\n\u001b[0;32m      7\u001b[0m max_epochs \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m40\u001b[39m\n\u001b[0;32m      8\u001b[0m optimizer \u001b[38;5;241m=\u001b[39m GradientDescentOptimizer(learning_rate)\n\u001b[1;32m----> 9\u001b[0m np\u001b[38;5;241m.\u001b[39mseed(\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m     10\u001b[0m \u001b[38;5;66;03m# 4. 训练神经网络\u001b[39;00m\n\u001b[0;32m     11\u001b[0m nn_NSA_plus \u001b[38;5;241m=\u001b[39m NeuralNetwork(input_size, hidden_size, output_size,optimizer, learning_rate)\n",
      "File \u001b[1;32mg:\\anaconda\\anconda2\\Lib\\site-packages\\numpy\\__init__.py:333\u001b[0m, in \u001b[0;36m__getattr__\u001b[1;34m(attr)\u001b[0m\n\u001b[0;32m    330\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRemoved in NumPy 1.25.0\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    331\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTester was removed in NumPy 1.25.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 333\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodule \u001b[39m\u001b[38;5;132;01m{!r}\u001b[39;00m\u001b[38;5;124m has no attribute \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    334\u001b[0m                      \u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;18m__name__\u001b[39m, attr))\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'numpy' has no attribute 'seed'"
     ]
    }
   ],
   "source": [
    "#实验1\n",
    "# 3. 初始化神经网络\n",
    "input_size = X_train.shape[1]  # 输入层大小（特征数量）\n",
    "hidden_size = 5  # 隐藏层大小\n",
    "output_size = y_train.shape[1]  # 输出层大小（三个类别）\n",
    "learning_rate = 0.08 # 学习率\n",
    "max_epochs = 40\n",
    "optimizer = GradientDescentOptimizer(learning_rate)\n",
    "np.seed(1)\n",
    "# 4. 训练神经网络\n",
    "nn_NSA_plus = NeuralNetwork(input_size, hidden_size, output_size,optimizer, learning_rate)\n",
    "train_loss_NSA_plus,nn_NSA_plus = nn_NSA_plus.train(X_train, y_train,\"NSA_plus\", epochs=max_epochs)\n",
    "\n",
    "nn_NSA = NeuralNetwork(input_size, hidden_size, output_size,optimizer, learning_rate)\n",
    "train_loss_NSA,nn_NSA = nn_NSA.train(X_train, y_train,\"NSA\", epochs=max_epochs)\n",
    "\n",
    "nn_GD = NeuralNetwork(input_size, hidden_size, output_size,optimizer, learning_rate)\n",
    "train_loss_GD = nn_GD.train(X_train, y_train,\"GD\", epochs=max_epochs)\n",
    "\n",
    "optimizer = AdamOptimizer(learning_rate,beta1=0.9,beta2=0.999,epsilon=1e-8)\n",
    "nn_Adam = NeuralNetwork(input_size, hidden_size, output_size,optimizer, learning_rate)\n",
    "train_loss_Adam,nn_Adam = nn_Adam.train(X_train, y_train,\"Adam\", epochs=max_epochs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a4b577b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y:1.4632291793823242,back:2.500869035720825,compare:2.2269883155822754,z:0.7028543949127197\n",
      "Function 'train_NSA_plus' took 7.9051 seconds to execute.\n",
      "Function 'train_NSA' took 7.1159 seconds to execute.\n",
      "Function 'train_GD' took 1.9451 seconds to execute.\n",
      "Function 'train_Adam' took 2.4736 seconds to execute.\n",
      "Function 'train_FISTA' took 6.7920 seconds to execute.\n"
     ]
    }
   ],
   "source": [
    "# 3. 初始化神经网络\n",
    "input_size = X_train.shape[1]  # 输入层大小（特征数量）\n",
    "hidden_size = 5  # 隐藏层大小\n",
    "output_size = y_train.shape[1]  # 输出层大小（三个类别）\n",
    "learning_rate = 0.07 # 学习率\n",
    "max_epochs = 1\n",
    "optimizer = GradientDescentOptimizer(learning_rate)\n",
    "\n",
    "# 4. 训练神经网络\n",
    "nn_NSA_plus = NeuralNetwork(input_size, hidden_size, output_size,optimizer, learning_rate)\n",
    "train_loss_NSA_plus,nn_NSA_plus = nn_NSA_plus.train(X_train, y_train,\"NSA_plus\", epochs=max_epochs)\n",
    "\n",
    "nn_NSA = NeuralNetwork(input_size, hidden_size, output_size,optimizer, learning_rate)\n",
    "train_loss_NSA,nn_NSA = nn_NSA.train(X_train, y_train,\"NSA\", epochs=max_epochs)\n",
    "\n",
    "nn_GD = NeuralNetwork(input_size, hidden_size, output_size,optimizer, learning_rate)\n",
    "train_loss_GD = nn_GD.train(X_train, y_train,\"GD\", epochs=max_epochs)\n",
    "\n",
    "optimizer = AdamOptimizer(learning_rate,beta1=0.5,beta2=0.5,epsilon=1e-8)\n",
    "nn_Adam = NeuralNetwork(input_size, hidden_size, output_size,optimizer, learning_rate)\n",
    "train_loss_Adam,nn_Adam= nn_Adam.train(X_train, y_train,\"Adam\", epochs=max_epochs)\n",
    "\n",
    "optimizer = GradientDescentOptimizer(learning_rate)\n",
    "nn_FISTA = NeuralNetwork(input_size, hidden_size, output_size,optimizer, learning_rate)\n",
    "train_loss_FISTA,nn_FISTA= nn_FISTA.train(X_train, y_train,\"FISTA\", epochs=max_epochs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cf988a91",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[9], line 12\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[38;5;66;03m# 4. 训练神经网络\u001b[39;00m\n\u001b[0;32m     11\u001b[0m nn_NSA_plus \u001b[38;5;241m=\u001b[39m NeuralNetwork(input_size, hidden_size, output_size,optimizer, learning_rate)\n\u001b[1;32m---> 12\u001b[0m train_loss_NSA_plus,nn_NSA_plus \u001b[38;5;241m=\u001b[39m nn_NSA_plus\u001b[38;5;241m.\u001b[39mtrain(X_train, y_train,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNSA_plus\u001b[39m\u001b[38;5;124m\"\u001b[39m, epochs\u001b[38;5;241m=\u001b[39mmax_epochs)\n\u001b[0;32m     14\u001b[0m nn_NSA \u001b[38;5;241m=\u001b[39m NeuralNetwork(input_size, hidden_size, output_size,optimizer, learning_rate)\n\u001b[0;32m     15\u001b[0m train_loss_NSA,nn_NSA \u001b[38;5;241m=\u001b[39m nn_NSA\u001b[38;5;241m.\u001b[39mtrain(X_train, y_train,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNSA\u001b[39m\u001b[38;5;124m\"\u001b[39m, epochs\u001b[38;5;241m=\u001b[39mmax_epochs)\n",
      "Cell \u001b[1;32mIn[6], line 89\u001b[0m, in \u001b[0;36mNeuralNetwork.train_option_decorator.<locals>.wrapper\u001b[1;34m(self, inputs, outputs, train_option, epochs)\u001b[0m\n\u001b[0;32m     87\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain_NSA(inputs,outputs,epochs)\n\u001b[0;32m     88\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m train_option \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNSA_plus\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m---> 89\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain_NSA_plus(inputs,outputs,epochs)\n\u001b[0;32m     90\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m train_option \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAdam\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m     91\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain_Adam(inputs,outputs,epochs)\n",
      "Cell \u001b[1;32mIn[4], line 6\u001b[0m, in \u001b[0;36mtime_it.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m      5\u001b[0m     start_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()  \u001b[38;5;66;03m# 记录开始时间\u001b[39;00m\n\u001b[1;32m----> 6\u001b[0m     result \u001b[38;5;241m=\u001b[39m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# 调用实际的函数\u001b[39;00m\n\u001b[0;32m      7\u001b[0m     end_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()  \u001b[38;5;66;03m# 记录结束时间\u001b[39;00m\n\u001b[0;32m      8\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFunction \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m took \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mend_time\u001b[38;5;250m \u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;250m \u001b[39mstart_time\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m seconds to execute.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "Cell \u001b[1;32mIn[6], line 201\u001b[0m, in \u001b[0;36mNeuralNetwork.train_NSA_plus\u001b[1;34m(self, inputs, outputs, epochs)\u001b[0m\n\u001b[0;32m    198\u001b[0m alpha \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m5\u001b[39m\u001b[38;5;241m/\u001b[39m(epoch\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m5\u001b[39m)\n\u001b[0;32m    200\u001b[0m \u001b[38;5;66;03m###y = (1-alpha)*x+alpha*z\u001b[39;00m\n\u001b[1;32m--> 201\u001b[0m nn_y \u001b[38;5;241m=\u001b[39m nn_x\u001b[38;5;241m*\u001b[39m(\u001b[38;5;241m1\u001b[39m\u001b[38;5;241m-\u001b[39malpha)\u001b[38;5;241m+\u001b[39mnn_z\u001b[38;5;241m*\u001b[39malpha\n\u001b[0;32m    203\u001b[0m time2 \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[0;32m    204\u001b[0m time_y \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m time2\u001b[38;5;241m-\u001b[39mtime1\n",
      "Cell \u001b[1;32mIn[6], line 36\u001b[0m, in \u001b[0;36mNeuralNetwork.__add__\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m     34\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__add__\u001b[39m(\u001b[38;5;28mself\u001b[39m,other):\n\u001b[1;32m---> 36\u001b[0m     temp_nn \u001b[38;5;241m=\u001b[39m copy\u001b[38;5;241m.\u001b[39mdeepcopy(\u001b[38;5;28mself\u001b[39m)\n\u001b[0;32m     37\u001b[0m     temp_nn\u001b[38;5;241m.\u001b[39mweights_input_hidden \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweights_input_hidden\u001b[38;5;241m+\u001b[39mother\u001b[38;5;241m.\u001b[39mweights_input_hidden  \u001b[38;5;66;03m# 输入层到隐藏层的权重\u001b[39;00m\n\u001b[0;32m     38\u001b[0m     temp_nn\u001b[38;5;241m.\u001b[39mweights_hidden_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweights_hidden_output\u001b[38;5;241m+\u001b[39mother\u001b[38;5;241m.\u001b[39mweights_hidden_output   \u001b[38;5;66;03m# 隐藏层到输出层的权重\u001b[39;00m\n",
      "File \u001b[1;32mg:\\anaconda\\anconda2\\Lib\\copy.py:162\u001b[0m, in \u001b[0;36mdeepcopy\u001b[1;34m(x, memo, _nil)\u001b[0m\n\u001b[0;32m    160\u001b[0m                 y \u001b[38;5;241m=\u001b[39m x\n\u001b[0;32m    161\u001b[0m             \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 162\u001b[0m                 y \u001b[38;5;241m=\u001b[39m _reconstruct(x, memo, \u001b[38;5;241m*\u001b[39mrv)\n\u001b[0;32m    164\u001b[0m \u001b[38;5;66;03m# If is its own copy, don't memoize.\u001b[39;00m\n\u001b[0;32m    165\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m x:\n",
      "File \u001b[1;32mg:\\anaconda\\anconda2\\Lib\\copy.py:259\u001b[0m, in \u001b[0;36m_reconstruct\u001b[1;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[0;32m    257\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m state \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    258\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m deep:\n\u001b[1;32m--> 259\u001b[0m         state \u001b[38;5;241m=\u001b[39m deepcopy(state, memo)\n\u001b[0;32m    260\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(y, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__setstate__\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m    261\u001b[0m         y\u001b[38;5;241m.\u001b[39m__setstate__(state)\n",
      "File \u001b[1;32mg:\\anaconda\\anconda2\\Lib\\copy.py:136\u001b[0m, in \u001b[0;36mdeepcopy\u001b[1;34m(x, memo, _nil)\u001b[0m\n\u001b[0;32m    134\u001b[0m copier \u001b[38;5;241m=\u001b[39m _deepcopy_dispatch\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mcls\u001b[39m)\n\u001b[0;32m    135\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m copier \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 136\u001b[0m     y \u001b[38;5;241m=\u001b[39m copier(x, memo)\n\u001b[0;32m    137\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    138\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(\u001b[38;5;28mcls\u001b[39m, \u001b[38;5;28mtype\u001b[39m):\n",
      "File \u001b[1;32mg:\\anaconda\\anconda2\\Lib\\copy.py:221\u001b[0m, in \u001b[0;36m_deepcopy_dict\u001b[1;34m(x, memo, deepcopy)\u001b[0m\n\u001b[0;32m    219\u001b[0m memo[\u001b[38;5;28mid\u001b[39m(x)] \u001b[38;5;241m=\u001b[39m y\n\u001b[0;32m    220\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m x\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m--> 221\u001b[0m     y[deepcopy(key, memo)] \u001b[38;5;241m=\u001b[39m deepcopy(value, memo)\n\u001b[0;32m    222\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m y\n",
      "File \u001b[1;32mg:\\anaconda\\anconda2\\Lib\\copy.py:143\u001b[0m, in \u001b[0;36mdeepcopy\u001b[1;34m(x, memo, _nil)\u001b[0m\n\u001b[0;32m    141\u001b[0m copier \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(x, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__deepcopy__\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m    142\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m copier \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 143\u001b[0m     y \u001b[38;5;241m=\u001b[39m copier(memo)\n\u001b[0;32m    144\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    145\u001b[0m     reductor \u001b[38;5;241m=\u001b[39m dispatch_table\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mcls\u001b[39m)\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#实验2\n",
    "# 3. 初始化神经网络\n",
    "input_size = X_train.shape[1]  # 输入层大小（特征数量）\n",
    "hidden_size = 5  # 隐藏层大小\n",
    "output_size = y_train.shape[1]  # 输出层大小（三个类别）\n",
    "learning_rate = 0.12 # 学习率\n",
    "max_epochs = 5\n",
    "optimizer = GradientDescentOptimizer(learning_rate)\n",
    "\n",
    "# 4. 训练神经网络\n",
    "nn_NSA_plus = NeuralNetwork(input_size, hidden_size, output_size,optimizer, learning_rate)\n",
    "train_loss_NSA_plus,nn_NSA_plus = nn_NSA_plus.train(X_train, y_train,\"NSA_plus\", epochs=max_epochs)\n",
    "\n",
    "nn_NSA = NeuralNetwork(input_size, hidden_size, output_size,optimizer, learning_rate)\n",
    "train_loss_NSA,nn_NSA = nn_NSA.train(X_train, y_train,\"NSA\", epochs=max_epochs)\n",
    "\n",
    "nn_GD = NeuralNetwork(input_size, hidden_size, output_size,optimizer, learning_rate)\n",
    "train_loss_GD = nn_GD.train(X_train, y_train,\"GD\", epochs=max_epochs)\n",
    "\n",
    "optimizer = AdamOptimizer(learning_rate,beta1=0.5,beta2=0.5,epsilon=1e-8)\n",
    "nn_Adam = NeuralNetwork(input_size, hidden_size, output_size,optimizer, learning_rate)\n",
    "train_loss_Adam,nn_Adam= nn_Adam.train(X_train, y_train,\"Adam\", epochs=max_epochs)\n",
    "\n",
    "optimizer = GradientDescentOptimizer(learning_rate)\n",
    "nn_FISTA = NeuralNetwork(input_size, hidden_size, output_size,optimizer, learning_rate)\n",
    "train_loss_FISTA,nn_FISTA= nn_FISTA.train(X_train, y_train,\"FISTA\", epochs=max_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "86e5e8b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y:0.3567049503326416,back:0.6481244564056396,compare:0.5768880844116211,z:0.16042470932006836\n",
      "Function 'train_NSA_plus' took 1.9640 seconds to execute.\n",
      "Function 'train_NSA' took 1.2394 seconds to execute.\n",
      "Function 'train_GD' took 0.3575 seconds to execute.\n",
      "Function 'train_Adam' took 0.5750 seconds to execute.\n",
      "Function 'train_FISTA' took 1.3802 seconds to execute.\n",
      "y:0.35999321937561035,back:0.6046888828277588,compare:0.5362412929534912,z:0.17072272300720215\n",
      "Function 'train_NSA_plus' took 1.8893 seconds to execute.\n",
      "Function 'train_NSA' took 0.9131 seconds to execute.\n",
      "Function 'train_GD' took 0.2633 seconds to execute.\n",
      "Function 'train_Adam' took 0.3751 seconds to execute.\n",
      "Function 'train_FISTA' took 0.9641 seconds to execute.\n",
      "y:0.30936694145202637,back:0.4962303638458252,compare:0.42281603813171387,z:0.12456440925598145\n",
      "Function 'train_NSA_plus' took 1.5355 seconds to execute.\n",
      "Function 'train_NSA' took 0.9282 seconds to execute.\n",
      "Function 'train_GD' took 0.2659 seconds to execute.\n",
      "Function 'train_Adam' took 0.4049 seconds to execute.\n",
      "Function 'train_FISTA' took 0.9926 seconds to execute.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_11688\\1227353846.py:5: RuntimeWarning: overflow encountered in exp\n",
      "  return 1 / (1 + np.exp(-x))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y:0.30712175369262695,back:0.4606802463531494,compare:0.442471981048584,z:0.13558506965637207\n",
      "Function 'train_NSA_plus' took 1.5089 seconds to execute.\n",
      "Function 'train_NSA' took 1.0388 seconds to execute.\n",
      "Function 'train_GD' took 0.2509 seconds to execute.\n",
      "Function 'train_Adam' took 0.3810 seconds to execute.\n",
      "Function 'train_FISTA' took 1.0708 seconds to execute.\n",
      "y:0.28528547286987305,back:0.48226308822631836,compare:0.413128137588501,z:0.14180469512939453\n",
      "Function 'train_NSA_plus' took 1.4992 seconds to execute.\n",
      "Function 'train_NSA' took 0.9717 seconds to execute.\n",
      "Function 'train_GD' took 0.2863 seconds to execute.\n",
      "Function 'train_Adam' took 0.3820 seconds to execute.\n",
      "Function 'train_FISTA' took 0.9897 seconds to execute.\n",
      "y:0.27791333198547363,back:0.45752787590026855,compare:0.4229750633239746,z:0.13873791694641113\n",
      "Function 'train_NSA_plus' took 1.4842 seconds to execute.\n",
      "Function 'train_NSA' took 1.0102 seconds to execute.\n",
      "Function 'train_GD' took 0.2534 seconds to execute.\n",
      "Function 'train_Adam' took 0.3736 seconds to execute.\n",
      "Function 'train_FISTA' took 0.9413 seconds to execute.\n",
      "y:0.31298279762268066,back:0.41766357421875,compare:0.44557714462280273,z:0.10873746871948242\n",
      "Function 'train_NSA_plus' took 1.4364 seconds to execute.\n",
      "Function 'train_NSA' took 0.9151 seconds to execute.\n",
      "Function 'train_GD' took 0.2485 seconds to execute.\n",
      "Function 'train_Adam' took 0.3719 seconds to execute.\n",
      "Function 'train_FISTA' took 0.9360 seconds to execute.\n",
      "y:0.25977253913879395,back:0.5157034397125244,compare:0.42165064811706543,z:0.14063739776611328\n",
      "Function 'train_NSA_plus' took 1.5092 seconds to execute.\n",
      "Function 'train_NSA' took 0.8949 seconds to execute.\n",
      "Function 'train_GD' took 0.2454 seconds to execute.\n",
      "Function 'train_Adam' took 0.3840 seconds to execute.\n",
      "Function 'train_FISTA' took 0.9864 seconds to execute.\n",
      "y:0.30890607833862305,back:0.5155689716339111,compare:0.4750537872314453,z:0.13337159156799316\n",
      "Function 'train_NSA_plus' took 1.6149 seconds to execute.\n",
      "Function 'train_NSA' took 0.9744 seconds to execute.\n",
      "Function 'train_GD' took 0.2523 seconds to execute.\n",
      "Function 'train_Adam' took 0.3691 seconds to execute.\n",
      "Function 'train_FISTA' took 1.0572 seconds to execute.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'NSA_plus': [[0.20858065759224018,\n",
       "   0.32812997557881207,\n",
       "   0.2209626055182568,\n",
       "   0.22264120281714384,\n",
       "   0.13500548793236156,\n",
       "   0.04994305080995671,\n",
       "   0.050828329563232265,\n",
       "   0.061312436728889315,\n",
       "   0.028027286866986672,\n",
       "   0.02228815599237274,\n",
       "   0.021230944532883135,\n",
       "   0.020546546705778494,\n",
       "   0.016570165984200972,\n",
       "   0.012837468235731099,\n",
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       "   0.04228968058442697,\n",
       "   0.04985605490242177,\n",
       "   ...]],\n",
       " 'GD': [[0.22242172220710696,\n",
       "   0.34003501389888846,\n",
       "   0.21218145541994096,\n",
       "   0.22281480667252013,\n",
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       "   0.011351407785393779,\n",
       "   ...]]}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#实验2\n",
    "# 3. 初始化神经网络\n",
    "input_size = X_train.shape[1]  # 输入层大小（特征数量）\n",
    "hidden_size = 5  # 隐藏层大小\n",
    "output_size = y_train.shape[1]  # 输出层大小（三个类别）\n",
    "learning_rate = 0.12# 学习率\n",
    "max_epochs = 2000\n",
    "key = [\"NSA_plus\",\"NSA\",\"GD\",\"Adam\",\"FISTA\"]\n",
    "train_dict = {key[i]:[] for i in range(len(key))}\n",
    "np.random.seed(1)\n",
    "for i in range(1,10):\n",
    "    optimizer = GradientDescentOptimizer(learning_rate)\n",
    "    # 4. 训练神经网络\n",
    "    nn_NSA_plus = NeuralNetwork(input_size, hidden_size, output_size,optimizer, learning_rate)\n",
    "    train_loss_NSA_plus,nn_NSA_plus = nn_NSA_plus.train(X_train, y_train,\"NSA_plus\", epochs=max_epochs)\n",
    "    train_dict[\"NSA_plus\"].append(train_loss_NSA_plus)\n",
    "    nn_NSA = NeuralNetwork(input_size, hidden_size, output_size,optimizer, learning_rate)\n",
    "    train_loss_NSA,nn_NSA = nn_NSA.train(X_train, y_train,\"NSA\", epochs=max_epochs)\n",
    "    train_dict[\"NSA\"].append(train_loss_NSA)\n",
    "    nn_GD = NeuralNetwork(input_size, hidden_size, output_size,optimizer, learning_rate)\n",
    "    train_loss_GD = nn_GD.train(X_train, y_train,\"GD\", epochs=max_epochs)\n",
    "    train_dict[\"GD\"].append(train_loss_GD)\n",
    "    optimizer = AdamOptimizer(learning_rate,beta1=0.5,beta2=0.5,epsilon=1e-8)\n",
    "    nn_Adam = NeuralNetwork(input_size, hidden_size, output_size,optimizer, learning_rate)\n",
    "    train_loss_Adam,nn_Adam= nn_Adam.train(X_train, y_train,\"Adam\", epochs=max_epochs)\n",
    "    train_dict[\"Adam\"].append(train_loss_Adam)\n",
    "    optimizer = GradientDescentOptimizer(learning_rate)\n",
    "    nn_FISTA = NeuralNetwork(input_size, hidden_size, output_size,optimizer, learning_rate)\n",
    "    train_loss_FISTA,nn_FISTA= nn_FISTA.train(X_train, y_train,\"FISTA\", epochs=max_epochs)\n",
    "    train_dict[\"FISTA\"].append(train_loss_FISTA)\n",
    "train_dict = {key:process_lists(train_dict[key]) for key in train_dict.keys()}\n",
    "train_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c02b4a1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1, 1, 1, 1, 1], [5.5, 4.7, 4.2, 4.7, 5.5], [10, 9, 9, 9, 10]]\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ee84ebb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "ind = 500\n",
    "# 创建一个示例数据\n",
    "y0 = [math.log(item) for item in train_loss_NSA_plus[0:ind]]\n",
    "y1 = [math.log(item) for item in train_loss_NSA[0:ind]]\n",
    "y2 = [math.log(item) for item in train_loss_GD[0:ind]]\n",
    "y3 = [math.log(item) for item in train_loss_Adam[0:ind]]\n",
    "y4 = [math.log(item) for item in train_loss_FISTA[0:ind]]\n",
    "x = [i for i,item in enumerate(y1)][0:ind]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2f732b2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# 使用 seaborn 设置美化参数\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# 创建一个新的图形\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# 绘制折线图\n",
    "plt.plot(x, y0, color='orange', linewidth=2, linestyle='-',label=\"NSA_plus\")\n",
    "plt.plot(x, y1, color='blue', linewidth=2, linestyle='-', label=\"NSA\")\n",
    "plt.plot(x, y2, color='red', linewidth=2, linestyle='-', label=\"GD\")\n",
    "plt.plot(x, y3, color='green', linewidth=2, linestyle='-', label=\"Adam\")\n",
    "plt.plot(x, y4, color='black', linewidth=2, linestyle='-', label=\"FISTA\")\n",
    "#plt.plot(x, y0, color='yellow', linewidth=2, linestyle='-', marker='o', markersize=5, markerfacecolor='yellow', markeredgecolor='yellow',label=\"NSA_plus\")\n",
    "#plt.plot(x, y1, color='blue', linewidth=2, linestyle='-', marker='o', markersize=5, markerfacecolor='blue', markeredgecolor='blue',label=\"NSA\")\n",
    "#plt.plot(x, y2, color='red', linewidth=2, linestyle='-', marker='o', markersize=5, markerfacecolor='red', markeredgecolor='red',label=\"GD\")\n",
    "#plt.plot(x, y3, color='green', linewidth=2, linestyle='-', marker='o', markersize=5, markerfacecolor='green', markeredgecolor='green',label=\"Adam\")\n",
    "#plt.plot(x, y4, color='black', linewidth=2, linestyle='-', marker='o', markersize=5, markerfacecolor='black', markeredgecolor='black',label=\"FISTA\")\n",
    "# 添加标题和标签\n",
    "plt.title('Loss Function', fontsize=20)\n",
    "plt.xlabel('X', fontsize=14)\n",
    "plt.ylabel('Y', fontsize=14)\n",
    "\n",
    "# 显示网格\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "# 显示图形\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7b3d1e19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def plot_shaded_lines(data_dict, max_steps):\n",
    "    # 创建一个图形\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    \n",
    "    # 遍历字典中的每个键及对应的数据\n",
    "    for key, value in data_dict.items():\n",
    "        # 获取最小值、平均值和最大值列表\n",
    "        min_vals, avg_vals, max_vals = value\n",
    "        \n",
    "        # 生成 x 轴数据，这里假设每个键对应的数据是按照索引顺序的\n",
    "        x = np.arange(len(min_vals))\n",
    "        \n",
    "        # 绘制阴影区域\n",
    "        plt.fill_between(x, min_vals, max_vals, alpha=0.3, label=f'{key} range')\n",
    "        \n",
    "        # 绘制平均值曲线\n",
    "        plt.plot(x, avg_vals, label=f'{key} average', linewidth=2)\n",
    "    \n",
    "    # 设置标题和标签\n",
    "    plt.title(\"Shaded Plot with Averages\")\n",
    "    plt.xlabel(\"Steps\")\n",
    "    plt.ylabel(\"Values\")\n",
    "    \n",
    "    # 限制x轴的范围\n",
    "    plt.xlim(0, min(max_steps, len(min_vals)-1))  # 限制最大步数为max_steps，防止超出数据范围\n",
    "    \n",
    "    # 添加图例\n",
    "    plt.legend(loc='upper left', bbox_to_anchor=(1, 1))\n",
    "    plt.yscale('log')\n",
    "    # 显示图形\n",
    "    plt.tight_layout()  # 调整布局\n",
    "    plt.show()\n",
    "# 最大步数\n",
    "max_steps = 550 # 控制x轴的最大步数\n",
    "\n",
    "# 调用函数绘图\n",
    "plot_shaded_lines(train_dict, max_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8143a770",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc82680a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8+OOP9t+FWq2mXr16DBo0iAceeKBAZswGDRrwySef8MUXXxAdHY2zs3ORz8jl5+bmxldffcXGjRtZtmyZfYhp7iTgd955J4MHD77mBNRCCCFAZZMZG4UQQgghhBCiQknyDyGEEEIIIYSoYBKYCSGEEEIIIUQFk8BMCCGEEEIIISqYBGZCCCGEEEIIUcGqZWBmtVqZNWsWt9xyC61bt7bP51KU5ORkXnrpJTp27EjHjh2ZMmUKWVlZ5dhiIYQQQgghRE1WLQOzL7/8koULF/Luu++yaNEiVCoV48aNw2g0Flr/2Wef5fz58/z444/MmjWL7du38/bbb5dzq4UQQgghhBA1VbVLl280GunSpQsTJ07kvvvuAyAtLY1bbrmFadOmMWTIEIf6+/btY9SoUaxZs4b69esDsG3bNh577DG2bNlCrVq1SnT9ffv2YbPZHCbtFEIIIYQQQtQ8JpMJlUpF27Ztr1u32vWYHTt2jMzMTLp06WLf5unpSbNmzdi9e3eB+nv27CEgIMAelAF06tQJlUrFv//+W+Lr22w2KlOsa7PZMBqNlapNonKTe0aUhNwvoqTknhElJfeMKKnKdM+UJDbQlnFbyt2lS5cACA4OdtgeGBhIbGxsgfqXL18uUNfJyQlvb+9C61+PTqfDZrM5BHoVKTs7m6ioKGrXro2Li0tFN0dUAXLPiJKQ+0WUlNwzoqTknhElVZnumVOnTqFSqYpVt9oFZtnZ2YASXOWn1+tJTU0ttP7VdXPrGwyGG2qDyWTi6NGjN3RsWYmKiqroJogqRu4ZURJyv4iSkntGlJTcM6KkKss9U1isUZhqF5g5OzsDyrNmucsABoOh0IjZ2dm50KQgBoMBV1fXG2qDTqejQYMGN3Rsacv9xiAiIqLCvzEQVYPcM6Ik5H4RJSX3jCgpuWdESVWme+bUqVPFrlvtArPcYYlxcXGEh4fbt8fFxdGkSZMC9YOCgvjjjz8cthmNRlJSUkqc+COXSqW64aCurLi4uFS6NonKTe4ZURJyv4iSkntGlJTcM6KkKsM9U9xhjFANk380adIEd3d3du7cad+WlpbGkSNH6NChQ4H6HTt25NKlSw7znOUe265du7JvsBBCCCGEEKLGq3Y9Zk5OTjzwwAN8/PHH+Pr6Urt2baZPn05QUBD9+vXDYrGQlJSEh4cHzs7OtG7dmnbt2vHCCy/w1ltvkZWVxZtvvsmwYcNuuMdMCCGEEEIIIUqi2gVmoEwYbTabef3118nJyaFjx4589913ODk5ERMTQ58+fXj//fcZPnw4KpWK2bNn8/bbb/Pggw+i1+sZOHAgkydPrui3IYQQQgghqjibzYbFYsFsNld0U2qM3AR+BoMBtbpsBwjqdDo0Gk2pnKtaBmYajYaJEycyceLEAvtCQ0M5fvy4wzY/Pz9mzZpVXs0TQgghhBDVnM1mIyUlhfj4eCwWS0U3p0axWq1otVouXrxY5oEZgLe3N0FBQSV6nqww1TIwE0IIIYQQoiJdunSJlJQUPD098fT0RKvV3vQHd1E8FosFg8GAXq8vtd6swthsNrKysoiLiwMKzqNcUhKYCSGEEEIIUYosFgupqakEBATg7+9f0c2pcXJ7KJ2dncs0MAPs6fjj4uIIDAy8qetVu6yMQgghhBBCVCSTyYTNZsPNza2imyLKQW5KfpPJdFPnkcBMCCGEEEKIMiBDF2uG0vo9S2AmhBBCCCGEEBVMAjMhhBBCCCFEkXr37k2vXr3IyMgosO+VV15hzJgx9vVVq1Zx77330rZtW9q2bcuIESNYuHBhoee1Wq306tWLFi1akJiYWCZtv7p9lZkEZkIIIYQQQohrio2N5YMPPrhmnaVLlzJlyhRGjBhBZGQky5YtY/jw4bz33nvMnj27QP0dO3aQkpKCn58fy5YtK6umVxkSmAkhhBBCCCGuKSwsjCVLlrB169Yi6/zyyy+MHDmSe+65h7p161KvXj1Gjx7NQw89xLx58wrUX7ZsGe3bt6dPnz4sWrQIq9Valm+h0pPATAghhBBCCHFNQ4cOpWvXrkyZMqXQIY0AarWavXv3kpqa6rB93LhxLFq0yGFbamoqf/zxB927d2fgwIHExMRcM+grTExMDI0bN2bVqlUMHTqUVq1acc8997Bv375r1t+5c6fD9saNGxMZGQlAdnY2r732Gt27d6dly5YMGzaM9evXl6hdN0oCMyGEEEIIIcQ1qVQq3nvvPdLS0nj//fcLrTNu3DiOHj3Krbfeyvjx4/nf//7HgQMH8PDwoG7dug51f/vtN4xGI/3796dDhw4EBgYW+Sza9bz33nuMHz+eX3/9lXr16vHoo49y4cKFGzrXZ599xvHjx/nf//7HmjVruPXWW3nhhReIiYm5ofOVhEwwXc0lX7hA/Lk49PgTEeGOu483qMt2oj0hhBBCCFGEc0vgwBtgSi//a+s8oNVUCB95Q4fXrl2biRMn8tZbbzFw4EBuueUWh/0DBgxg0aJFzJ8/n23btrFlyxYAIiIimDZtGu3bt7fXXbZsGW3atCE0NBSAwYMHM3/+fGJjYwkODi5Rux5//HFuv/12AKZOncrff/9NZGQkEydOLPF7PHfuHO7u7oSHh+Ph4cFzzz1Hhw4d8PLyKvG5Skp6zKqxzydPJrxRIwYNH0yXDrcSGJLFlvdGQMI/Fd00IYQQQoia6ch0SDsG2RfKv6Qdg6PTb6r5o0aNuuaQxlatWjF9+nR27NjBypUreemll8jMzGTcuHH2zIvHjx/n8OHDDBo0yH7ckCFDsFgsLF68uMRt6tSpk31Zp9PRvHlzTp48eQPvLq/Xr2vXrowZM4ZvvvmGunXr4uHhcUPnKwkJzKqxbStXkvsIZSbnyDb+wdyNQ+HUNxXaLiGEEEKIGqvZJPBsAi61y794NoGmJe9Fyq+oIY2XLl1i6tSpXL582V6vcePGjB8/nrlz55KZmcnu3bsB7BkYP/zwQ5o1a0azZs0YNWoUoGR2NJvNJWqTVus4CNBqtaLRFD1CzGaz2ZdNJpPDvrZt27JlyxY+++wzGjduzNKlSxk4cCB///13idp0I2QoYzX2YYcO/HXkCJfsWy5xPrEDZEZXYKuEEEIIIWqw8JE3PJSwsqhduzaTJk3izTffJCwsjODgYJycnFi0aBFBQUGMGzfOob67uzsA/v7+mEwmVq1aRY8ePfi///s/h3pr167lyy+/ZNOmTfTv37/Y7Tl48CCNGzcGwGg0cuTIEYYNG1agnk6nA3Do6Tt37pxDnVmzZtkzRfbp04fJkyczZMgQ1q1bR9euXYvdphshgVk1FuHuzmLgVvuWeM4nhkHW+YprlBBCCCGEqPJGjRrFunXr2LFjB8HBwfj6+vLYY48xc+ZMMjIyGDhwIO7u7pw6dYovv/ySzp0706FDB9avX09SUhIPP/wwjRo1cjhnSEgI8+fPZ8GCBSUKzGbOnIm/vz9hYWF89dVXZGdnM3z48AL1AgMDCQsL44cffiAiIoLs7Gzef/99nJyc7HWio6NZuXIlU6dOJTw8nP/++4+LFy/Stm3bG/9hFZMEZtWZzUagw4Z4zieFYcs8j8pmA5WqghomhBBCCCGqunfffZc77rjDvv78888TERHB4sWL+fnnn8nJySE4OJjBgwfz+OOPAxAZGUlERATdu3cvcD53d3fuuecevv/+e6Kjo6lTp06x2jFq1Cjef/99Ll26ROvWrZk7dy4BAQEF6qlUKqZPn857773HsGHDCAkJ4dlnn+Wzzz6z13n77bf58MMPmThxIikpKdSuXZuXX36ZO++8s6Q/nhJT2fIPshQ37eDBgwC0bNmyglsCTJhA0tdf42ff0B9YR+LXvvg+eBL0fkUfK2qsrKwsjh49StOmTXF1da3o5ohKTu4XUVJyz4iSqor3TE5ODmfPnqVu3bo4OztXdHOqrZiYGPr06cO8efPo3LmzfbvFYiEnJwdnZ+drPmtWWq71+y5JbCDJP6oxo8GKN5B3O8YDyHBGIYQQQgghKhkZyliNZaea8AIC4EoCkLzArHXmefBpU2FtE0IIIYQQoihDhw7l/PlrdyTcSGr9ykwCs2osJyXzqsDsMmDjfJL0mAkhhBBCiMprzpw5BVLZXy08PJzjx4+XU4vKngRm1ZghWZlRPu/RRxOQLkMZhRBCCCFEpRYSElLRTSh38oxZNWZKzwS4KjNjrARmQgghhBBCVDLSY1aNmTMNQP4eM4Am/PZfe8zprvLLF0IIIYQQopKQHrNqLFqXztODIdPLcXtK5r+s2FR9xuMKIYQQQghR1UlgVo193OMiX3SCX7oU3PfD+iSwWcu/UUIIIYQQQogCJDCrxlz9IgDI8Sq47/cDZi5FHSrfBgkhhBBCCCEKJYFZNfbiy7OUBbeC+yxWWLxwXvk2SAghhBBCCFEoCcyqsR7hPQhLpdDADODAgf944YUXmDFjRrm2SwghhBBCVB29e/emV69eZGRkFNj3yiuvMGbMGPv6qlWruPfee2nbti1t27ZlxIgRLFy4sNDzWq1WevXqRYsWLUhMTCyz9lcVEphVY2qVmkf2Aa6F7/9u4UZmzpzJyy+/zNq1a8u1bUIIIYQQouqIjY3lgw8+uGadpUuXMmXKFEaMGEFkZCTLli1j+PDhvPfee8yePbtA/R07dpCSkoKfnx/Lli0rq6ZXGRKYVXPjnvgRVw32ycyc3fsXWm/u3Lnl1yghhBBCCFGlhIWFsWTJErZu3VpknV9++YWRI0dyzz33ULduXerVq8fo0aN56KGHmDev4CM0y5Yto3379vTp04dFixZhtdbsxHQSmFVzPu178Owu4BFgLGjGbSu03sWLF8u1XUIIIYQQouoYOnQoXbt2ZcqUKYUOaQRQq9Xs3buX1NRUh+3jxo1j0aJFDttSU1P5448/6N69OwMHDiQmJuaaQV9NIHMMV3eenryxBdbXh731IJMs0AEmx2oSmAkhhBBClL0lS+CNNyA9vfyv7eEBU6fCyJElP1alUvHee+9xxx138P777/Pee+8VqDNu3Dief/55br31Vjp37kyHDh3o0qULLVu2xNPT06Hub7/9htFopH///oSEhBAYGMjChQvp2bPnjb69Kk8Cs+rO1RUXM2ycB7ffD9vDARcKBGYXLlzAarWiVksnqhBCCCFEWZk+HY4dq9jr30hgBlC7dm0mTpzIW2+9xcCBA7nlllsc9g8YMIBFixYxf/58tm3bxpYtWwCIiIhg2rRptG/f3l532bJltGnThtDQUAAGDx7M/PnziY2NJTg4+MYaWMVJYFbdqVQAeOfAuvkw5OVgtjjHQppjtZycHM6cOUODBg0qoJFCCCGEEDXDpEkwZUrF9ZhNnHhz5xg1ahTr1q1jypQp/PbbbwX2t2rViunTp2Oz2Thx4gRbtmxh3rx5jBs3jg0bNuDn58fx48c5fPgwkydPth83ZMgQfvzxRxYvXsxzzz13c42soiQwq0HcTLA0ZyihHj9iiDMU2H/o0CEJzIQQQgghytDIkTfeY1UZXD2kMdelS5f45ptvGD9+PLVq1UKlUtG4cWMaN25Mnz59GDx4MLt372bgwIH2DIwffvghH330kcP5ly5dylNPPYVWW/PCFBm3VsP4u/jRpUGXQvcdPHiwnFsjhBBCCCGqmtq1azNp0iSWLl3Knj17AHBycmLRokWsXLmyQH13d3cA/P39MZlMrFq1ih49erBixQqWL19uL08++SRxcXFs2rSpXN9PZVHzQtGazsWF+rXrs4UtBXYdPXq0AhokhBBCCCGqmtwhjTt27CA4OBhfX18ee+wxZs6cSUZGBgMHDsTd3Z1Tp07x5Zdf2pOBrF+/nqSkJB5++GEaNWrkcM6QkBDmz5/PggUL6N+/8CmeqjPpMasBYh95JG9FpcLHx6fQepcvXy6nFgkhhBBCiKru3Xffxc3Nzb7+/PPP895777F7927GjBnDoEGDmDZtGt26dWPOnDkAREZGEhERQffu3Qucz93dnXvuuYe///6b6OjocnsflYX0mNUAGW3a5K2YzUUGZkeijpRPg4QQQgghRJVR1NDC2rVrs3fvXodtw4YNY9iwYUWeKzdAK8qkSZOYNGlSidtYHUiPWQ1g02jyVkwmfH19C6136fIl/r34bzm1SgghhBBCCJFLArMawCEwu0aPGVkwcuG9pBsqIH+rEEIIIYQQNZgEZjWALX+6UbO5yB4zbBB16TRPr326fBomhBBCCCGEACQwqxEcAjOTqZAeszp5i5kwb/885u2fVy5tE0IIIYQQQkhgVjNcNZSxYI9Z07zFLOVl3Kpx/Bn1Z1m3TAghhBBCCIEEZjXC1UMZC/aYNbEvBSX0AcBoMXLnwjs5cPlAObRQCCGEEEKImk0Csxrg6qyMXl5eV9XI6zHzPjOUwfUHAJBmSKPf/H4SnAkhhBBCCFHGJDCrAa7uMdPkD9QACLUvpSYnsbh+OJ2dlfW4zDh6/diLPRf3lH1DhRBCCCGEqKGqXWAWGxvLiy++SPfu3enYsSOPPvooJ0+evOYxs2fPpnHjxgWK2Wwup1aXrat7zAA6dux4ZUMbINC+Oz3tIm5nv2FtCPbgLDknmd5ze7Pt3LZyaa8QQgghhBA1TbUKzIxGI+PHjycxMZGvv/6aX375BQ8PDx588EGSkpKKPO748ePceeedbNu2zaFo8/c0VWFXz2MGsHTpUt56tAfwK/kDs+zseAB8NLChNtzqogIg3ZjOgJ8GsOH0hvJqthBCCCGEEDVGtQrM9uzZw4kTJ/joo49o0aIFDRs25KOPPiIrK4tNmzYVedyJEydo1qwZAQEBDqW6uHooI0B4eDhvTmgDRAB579ViTSTL4AKAhxrWhtjoX7sdAFmmLG5fcDsrjq0on4YLIYQQQohKwWazERkZyZgxY+jSpQstWrSgb9++vPPOO1y+fNle7+oRaC1btqRfv358+umnZGdnV+A7qPyqVWDWsGFD/ve//1GrVi2H7TabjdTU1EKPyc7O5ty5czRo0KA8mlghbDpd3kpOTt6yzpM37nobcAHcr2yMJz4tAPw6AeCqhpWtunNXk7sAJVvjiMUjWHBwQbm0XQghhBBCVCyLxcITTzzBBx98wG233cb8+fNZv349U6ZM4fDhw4wYMYKEhAR7/VdffdU+Au23337j2WefZdmyZUyYMKHaPCpUFqrHWL0rAgIC6Nmzp8O2efPmYTAY6N69e6HHnDx5EqvVyu+//84777yD0WikU6dOvPzyywQGBhZ6zPXYbDaysrJu6NjSlp2djVWvt69bMjMxXGmbFhem3PUW/VuuZ8DHvmRmZQBxxKfXJShsLE6Je1BhRXd5Az8O2YlerWfhkYVYbBZGR44mJTOFB1s9WEHvTJSV3G+z5FstURxyv4iSkntGlFRVvGcMBgNWqxWLxYLFYqno5ty07777jq1bt7Jw4UKaNWtm316rVi3at2/P7bffznfffcfLL78MgJubm33eXF9fX0JDQ6lTpw733HMPkZGRjBgxokzba7PZ7K/l8fO3WCxYrVblc7fVWqAtKpWqWOepUoFZTEwMffr0KXL/tm3bHIYgrl+/nk8//ZQxY8bQpEmTQo/JTQzi4eHBrFmzSEhI4JNPPmHs2LH8+uuvuLi4lLidJpOJo0ePlvi4MpMvMMtJSuLYlbb5p2RSR2Ohe+MduLs2JTMLIInY5A64JXsS5twC95wDqNOPEXVoKy/WfRFjhpHIc5HYsPHUuqdIiU+hf0j/inlfokxFRUVVdBNEFSL3iygpuWdESVW1e0ar1WIwGCq6GTfNZrPx888/M3jwYOrVq0dO/tFXgEql4ptvvsHf39++z2QyFahXv3592rRpw6pVqxgyZEi5tL28fv4GgwGz2cyZM2cK3e/k5FSs81SpwKxWrVqsWbOmyP25kTnAggULmDp1KoMHD2by5MlFHjNixAj69u3rMLdXw4YN6dmzJ5s3b2bw4MElbqdOp6s0QyOzs7OJiorC5uKCKjsbF6BpU2XeMk1MY7gyJDjQ15fLV3qgT8W506d5L3S6vXBKmcOsUTBYA5ozr+k8QjaHMPvf2diw8eZ/b9K0XlP615XgrLrIvWciIiJu6IsJUbPI/SJKSu4ZUVJV8Z4xGAxcvHgRvV6Ps7Oz486lS1G/9Rakp5d/wzw8sL79NpSgx+r8+fNcunSJHj16FHwvV9SrV89hXafTFVq3SZMmrF27tsjzlBabzYbBYECv1xe7t+pmabVawsPD0efrEAE4depU8c9R2o0qSzqdjvr161+33scff8w333zDmDFjeO211677C7l6wuVatWrh7e3NpUuXbqidKpUKV1fXGzq2zLi4QHY2aoMhr21ueb2LtQM8OXhCWT4T74arVyh45M1v5kwmXDlu1pBZ5Fhz+Hbft5isJu5ffj+bHtxEl9Au5fZ2RNlzcXGpfPexqLTkfhElJfeMKKmqdM+o1WrUajUajabg/LEzZsCxYxXTMEAzYwbcc0+x6ycnJwPg7+/v8F4mTJjAzp077eshISGsXr0awP7er+bl5UVGRkah+0pT7vBFlUpV5tcC0Gg0qNVqXFxcCgSdJQkMq1RgVhzTp0/n22+/ZdKkSTz66KPXrT9jxgw2btzI6tWr7T+4mJgYkpOTK02vV2mwOTujAsj/7JsuLyANC8zrYj2X4gYqFej98+oa8h7oVKlUzLl9DqmGVJYcWUK2OZs7F97Jrsd2Uce7Thm+CyGEEEKIKm7SJJgypcJ6zJg4sUSH+Pj4AJCSkuKw/e2337YPV5w/f/41M6DnSk9Px93d/br1aqpqFZjt3LmTb7/9ljFjxjB06FDi4+Pt+1xdXXFzc8NoNJKamoqXlxdOTk4MHDiQH3/8kalTpzJmzBgSEhKYNm0a7dq145ZbbqnAd1PKch9EvHgRsrOVHjSdp313ff8M+3Js6pW6er+84w2JDqfTqDXMv2s+CVkJbI7aTFxmHEMXDmX7I9txd5J/cEIIIYQQhRo5UilVRFhYGAEBAezatcvh2bD8WdCvHn1WlMOHD9O8efNSb2N1Ua3S5f/222+AErX36NHDoXz//fcA7Nu3jx49erBv3z4AmjdvzrfffsvRo0cZPnw4Tz/9NE2bNmXOnDnlNia1PKjzD8vMHVfslPePqKnfaftyfOqVrEdF9Jjl0mv1LL1nKQ18lZ7FA5cP8EDkA1ht1gJ1hRBCCCFE1aPRaBg7dizLly/nWBFDMGNjY697nkOHDvHff/9xxx13lHYTq41q1WM2depUpk6des06nTt35vjx4wW2LVhQg+blWrsWjEaHHrMWgVH25aR0ZSwxTvl7zAoGZgC+Lr6sum8VXb7tQqohlRXHV/DR9o94pccrZdFyIYQQQghRzh577DGOHDnC/fffz/jx4+nVqxfu7u6cOHGCn376ie3btzukwE9PT7ePXMvKyuLAgQPMmDGDzp07M3To0Ip6G5VetQrMRNFszs6o8qctvXQJQmvbV0PzxWCZWXHKgnO+HjOj41DG/Jr4N2HRyEUM+nkQNmy8vul1uod155Y61WgoqBBCCCFEDaVWq5k5cyZr165l2bJlzJs3j7S0NPz9/enQoQM//fQTHTt2tNefNm0a06ZNA8Dd3Z3Q0FBGjx7N2LFjyyUZR1UlgVkNYVi7FufbbsvbsHYtPP44aN3AnImTFpy0PhjNyVisMaSmgpenF6jUYLMW2WOWa0CDAUy5dQrv/PUOFpuF+5bdx77H9xGQL/OjEEIIIYSougYNGsSgQYOuWefqkWmi+KrVM2aiaNZOneCRR/I2TJigvObLzOjhnLscy+nTJiUoyx3OmD/5R04CJP8HV2ZVz/VGzzfoFdELgAvpF3hw+YP2mdeFEEIIIYQQRZPArCa5Ov2/1erwnJm/R+68Cza2bj3Ik08+yYw18PdJeGP+BQ4cOADGVPi9HaxtCye/cjidRq3hl+G/EOgWCMDaU2v5bt93ZfmOhBBCCCGEqBYkMKtJrp7XbfVqcPK1r9b2zbsdnn/+Ib766ite/j6ebm/B1GUm7rprGLZjn0HWeaXS4fcKXCLYI5gf7vzBvv7iuheJToku1bchhBBCCCFEdSOBWU0SGOi4PmECNBgHah24hlG3bni+nQcLHH7mzFnO7crXA5Z9sdDLDG44mEfaKMMm043pPLryURnSKIQQQgghxDVIYFaTXbwIYaPhXgMMO0erfuOue8jug+fyVlQasJoKrffJgE8I9QwFYOPZjfx88OdSabIQQgghhBDVkQRmNU2fPo7rUVFwZSLt0aMHAxEOuwf1+IDlL+St3z0L6j0Pv+4GbBbIOFvoZbycvfj69q/t6y+vf5mUnJSbbb0QQgghhBDVkgRmNc2sWY7rGRn2RT8/Z+BDh91ZhmF0qu94yNl4GD4TVKOhSbs+REVFkZOTw4YNG1ixYgVWqxVQhjTe1eQuAC5nXmbKpiml/W6EEEIIIYSoFiQwq2kCrppXbPFih9WePe8G7rmyNo3jUWEE+0Btn8JPd/x0DM888wyhoaH079+fYcOG8corr9j3zxw4E1edKwBf7vmSA5cPlNIbEUIIIYQQovqQwKym8fNzXP/gA4fVL79U8dRTC1CrU4FXuBTvSnKmNwGeFOm3334jMTFvnrNPPvmEw4cPAxDuFc6UW5WeMqvNyqQNk0rlbQghhBBCCFGdSGBW06jV8MILRe5u1gxmz1YzfrwnoDx7dvRCU0Z1zavzypPD+GF80ZewWCy8kO8az3d5nnAvJePjutPr2HB6w029BSGEEEIIUf4yMjJo3bo13bp1w2g0Xrf+mDFjHEZSiWuTwKwmmjzZcd1sLlCladO85X4fbKBP8wgGt4HR3eGNaV/yUF9P2kY4HhMeHk5EhLJxw4YNTJgwgbCwMO4dcS8PeD5grzfpj0lYbdbSeS9CCCGEEKJcrF69Gj8/PzIyMtiwQb5oL20SmNVEAQEwdGje+rFjBao0b563nGVw47l5P7F6Ivw0qR4uXsHg0ZDmtR2P6dq1KyNGjLCvf/3118TExLBy5Uo+GPcBDVIbAPDfpf/4+YCkzxdCCCGEqEqWLVtGjx496Nq1KwsXLqzo5lQ7EpjVVL165S1HRhbY3bMn9O+ft/7PqS5k5LiBT1tlg0dDWoQ5HtO2bVu6d+9e6OWsVisJ8xIgXVl/bdNr5JhzbuINCCGEEEKI8nL69Gn2799P9+7dGThwILt27eL06dP2/UajkWnTptG1a1c6dOjAjBkz7Jm6c23atIlRo0bRtm1bWrZsyciRI9mxY4d9/5gxY5g5cyZTpkyhbdu2dOnShS+//JIzZ84wevRoWrVqxdChQzlwoHomk5PArKYaPlx53gwKZGYE0Gph3Tq4915l3WrTMPqLn3l74bMYDIBHowI9Zu3atSs0MPPy8gIgJTGFusfrAnA+7Txz9swptbcjhBBCCCHKztKlS3F1deXWW2+lb9++ODk5sWDBAvv+d999lzVr1vDBBx+wYMECLl68yJ49e+z7Dx06xFNPPUX//v1ZuXIlS5Yswc/Pj5dfftnhebVvv/2W4OBgVq5cyZgxY/jss894/PHHeeSRR1iyZAl6vZ633nqrPN96udFWdANEBalTB1q3hn374PBh2L0bOnYsUG3AAFi0SFleufdOVu6Fup1h7K1NCvaY1dXiHxhIREQEUVFRALi5uXH48GHCwsKw2WwkbE2A/4AceGXlK9zy4y20b9m+TN+qEEIIIURlseTwEt748w3SDenlfm0PvQdTb5vKyGYjS3Sc2Wxm1apV3Hbbbbi4uADQs2dPVqxYwUsvvYTFYiEyMpI333yTnj17AjBt2jR27txpP4dGo+H1119n9OjR9m1jx47lkUceITExkeDgYAAaNWrEk08+CcAjjzzCrFmzGDx4MH369AFg+PDhTJs27cZ/CJWYBGY12fDhSmAGMGmS8mBZly7wQF6iji5dCh72668w9v47CW/aE9hi3+6fsx24DQ8PD/u2WrVqUbt2bTp06MDu3btJT02HVGWfIcFAty7dWPzLYu68884yeINCCCGEEJXL9B3TOZZQ8Pn+cpGuXL+kgdmWLVuIj49n8ODB9m2DBw9mw4YNrF69msaNG2MymWjZsqV9v16vp2m+bHJNmzbFy8uLb775hrNnzxIVFcXRo0cBJaN3rrp169qXc4PAsLC83gC9Xl+sjJBVkQRmNdlLL8HMmZCYCH/+qZQvvoDTp+HNNwFo3LjgYatXQ0QDV0aP/pOvPp3Kpx+9wZt3AZf+gBav88orr9i/Dcn9RmPgwIHs3r27wLmMWUZGjBjBokWLHBKHCCGEEEJUR5O6T2LK5ikV1mM2sdvEEh8XeSUfwbPPPltg38KFC3nzyufGq2m1eaHG7t27eeSRR+jZsycdOnRgyJAhZGdn89RTTzkco9PpCpxHra4ZT19JYFaTubjAo4/CRx85bn/rLXtgplZDo0Zw4kTebpMJoqNh2jRIS5vChHpzIeM0JOwAUwb33nsv0dHRaLVa7r77bkAJzKZOnWo/R+thrdl/cj8cVr4lue+++9i+fTsdCxlOKYQQQghRXYxsNrLEPVYVKSkpiS1btjB8+HAefvhhh31z585l6dKlGI1G9Ho9//77L02aNAGU4Y/Hjh2jc+fOAHz33Xd07tyZ2bNn24+fP38+ADabrZzeTeVWM8JPUbRHHrlulXffBY2m8H3nzwNBfZUVqwnit6LRaJg8eTITJ060f8PRqVMnvL297cfNnzEfl1Eu0EZZN5lMTL56fjUhhBBCCFGhVqxYgdls5rHHHqNRo0YOZcKECWg0Gn799VceeOABZs2axfr16zl9+jRvvvkmly9ftp8nODiY48ePs2fPHmJiYli2bBmfffYZQLUdmlhSEpjVdA0bFr49Odm+ePfdEB8Pt91WsFpMDBDUL29DbOGTDWq1Wr766itatWrFvHnzaFmvJS90fQHuAHyUOhs3bmTLli2FHi+EEEIIIcpfZGQk3bp1o379+gX2hYWF0a9fP1avXs0TTzzB/fffzzvvvMPIkSOx2Wz07t3bXvfZZ5+lTZs2TJgwgWHDhrFkyRKmTZuGs7NztU1/X1Iqm/QdlqqDBw8CODz8WJGysrI4evQoTZs2xdXVtfBKQ4fCqlWO2159Fd57z2HTuHHw7beO1b79Fh59IAmW+QM28G4Jg4v3jyslJ4V6n9UjeWcy/Kpsa9q0Kdu2bcPX17dY5xClr1j3jBBXyP0iSkruGVFSVfGeycnJ4ezZs9StWxdnZ+eKbk6NY7FYyMnJwdnZGU1Rw75K0bV+3yWJDaTHTMCQIQW3FZKGtF69gtXOnwf0vuDbQdmQchCyLhbrst7O3rx6y6vQEghUth09epTGjRvz4osvcubMGTZv3sxff/3FxYsXWbhwIadOncJmsxETE2M/z9WTFwohhBBCCFHVSPIPocxpdrUrc1Dkly97qd3582CzgSpkICRdybp4cQ00eKxYl36q41PM/GcmF0ZdgO+ATEhISODTTz/l008/veaxzZs3R6/Xc+7cOT7++GN27dqFVqvl/fffJzU1FW9vb3uaVSGEEEIIISoz6TET0K5dwW35njHLVVhg9v33EBAAW86Oytt4cU2xL+2ic+GtXm+BLzAW3Ou7o1KpinXs4cOH2bt3LwkJCTz00EN8+eWXzJo1Czc3N0JCQmjbti3JhbwPIYQQQgghKhsJzAQEBkL//o7bDh5UMn7kU9hQRlCmQXticlPQBygbLm0Ai6HYl3+ozUM08W8CtSBjTAafbPiEl19+mfDwcAYOHMgdd9xBx44dSzyHxfHjx/H19aV37972+TeEEEIIIYSojCQwE4qVK+G//yB34kCbDQ4fdqgSEABjxkBhz7AePaqC4IHKijkD4rcW+9JatZbZg/LmtJj671T+7+3/Izo6mrVr17Jy5Up27dqF2WxmyZIlfP311+zatYtBgwbx0EMP0bp162uef/PmzYwaNUoy/gghhBBCiEpLAjOh0OuhdWto0CBvW1RUgWrz5kFaGuTLfmr30CdvM2/rGGXlwuoSXb5PvT7c1+I+AJKyk5i0YVKBOiqVipEjRzJ+/Hg6duzImjVr+OGHH/jnn39YtmwZly9f5q233qJVq1YMGjTI4ViTyUTr1q154okniI2NLVHbhBBCCCGEKGsSmAlHERF5y4UEZgA6HWgLSRszd1ldHpwzj31RbeDsXMiMLtGlPxnwCZ56TwB++O8HVh5fWazjnJ2dGT58OIGBgbz55pvs37+f1atX8/333zNx4kSHtKVz5szhgQceICEhwSGzoxBCCCGEEBVJAjPhKH+Gj7Nni6ym1xd9isjdw8GYDNvuBUvxZ3IPcg/i434f29cfXfkolzIuFfv4/FQqFQ8//DAfffQRu3btIjw83L5v06ZNBAQE0LBhQyZNmkSDBg0YOnQoH3zwAT4+PkyaNIknn3ySiIgIFixYwF133cUtt9zC5s2bue2223jkkUf4/fff6dq1K59//jmff/45PXr04I8//uCxxx6jX79+7Nixg169ejFq1Ci2bNlC9+7dmTFjBtu3b+e5557j2LFjN/S+hBBCCCFE9SQTTJeyKjnBdH4ZGeDhoSzfeits2VJotS1boFevok/z0K0/8PzAmbQe2B+aToSDbynJQZpNAq1bkcfZbDbuWnQXK46vAGBA/QGsvn81GvXNTQ5os9mYN28eDz300E2dp7R06NCBoKAg9u/fz/Lly2lXWGbMClIVJ/IUFUfuF1FScs+IkqqK94xMMF2xZIJpUT24uytZGgEOHACzudBqPXsqu0+ehML+Rv7418M8P38mHJ0BG26Bk1/BoXfg9w7KJNRFUKlUfDv0W4LcgwBYd3odkzdOvtl3hUqlYuzYsfS/OvtkBdmzZw+//fYb58+fZ8yYMXTo0IFevXqRkZFBTk4O8n2JEEIIIUTNIoGZKCi3KywlBQ4dKrJay5ZKrpBPPoFatQru//PobYAN0k/kbUw7pgRqaSeLPK+/qz8/3fUTGpXyDcf0HdP5bu93JX8fV1GpVCxatIhPP/2U33//nZCQkJs+Z2k4cuQI//77L1u2bKFfv354eXkxcOBAli1bRt++ffn99995/fXX6d27NwcPHuTpp5/m6aefxmAwsGXLFi5cuFDRb0EIIYQQ1diYMWNo3LhxoeW9994jMjKSxo0bOxxz9uxZXnzxRbp27UqLFi3o3bs3b7/9NgkJCQDExMQUec7c8vnnn9vPt3jxYho3bsy0adPK9b2Xp0JSOIgar0mTvOVL13/G6/HHlbJzJ3Tp4rgvx6jH2ckAOi9wqwMpB8CUCn/dCQP+AZ1noefsU68Pnw/6nCfXPAnAhNUT8Hb2ZkSzETf8tgC8vb15/vnnAdi5cyd//PEHw4cPZ8OGDZw4cYIHHniAV155BXd3d/r27cuzzz5L79690Wg0zJ8/n9GjR7Nt2zaio6O5/fbbWbt2LQBNmjTh8OHDODs7Y7VaMRqL/2xdfv/88w8A69evZ/369QBs3LjRvr9Vq1b25U2bNnH06FECAwP59ddfWbp0KQ888EClGhYphBBCiOph0KBBvPbaawW2u7i42D+z5EpISOC+++7j1ltv5ZtvvsHHx4ezZ88yffp0xowZw4oVKwgODmbbtm32Y77//nvWrFnD0qVL7dvyD12NjIykbt26LF++nBdffLFaDhGVwEwUFBCQt3z6dLEP69xZSac/dmzetpjsTjRwPwQ9loJfR1jfBVKPQNpR2PUEdP+5yPM90fEJjiUcY9auWZitZu5dei8/Df+JUS1G3ci7KiA0NNT+zNmIEXkB388/57Xp7rvvBpRn1GbOnImPjw+pqanEx8fTsGFDDl3pUWzatCkrVqygadOmuLu789NPP3Hvvffyww8/sGnTJrp378706dNLpd25jh49CkBcXBzdu3cHYNWqVbz33nts3ryZV199lbCwsFK9phBCCCFqJmdnZwLyf0a8ht9//x2z2cyHH36ISqUCoHbt2oSEhDBo0CC2bt1Knz59HM7n6uqKRqMp9BqnT59m3759fPHFFzzzzDOsWbOG4cOHl84bq0QkMBMF5f8H8fTT8NRTxT50zBjYvx9mzFDWYxpvpkFPM2iupHG8dQX83hFMKRD9CzR/FbybF3m+TwZ8QpoxjR//+xGLzcLoyNGcTz3Py91etv9DLxVWE6i0oFJB1kUwJoF7XTAkQOIuVBo3fN3qwNmVeKefwtu9PpzdSYvMLcqxJ1owPOwsnP8YXEKY3D8UTj3A1E4JTO3XDJvlMP6XPMgyaVmzz8Lu42ml1/Z8Tp06xb333gtA5uFvebh/ABnZRm5vp0HVfDI0eb5MriuEEEIIkUulUpGZmcnOnTvpkm84Vb169Vi9ejXBwcElOl9kZCSenp706tWLDh06sGDBAgnMRA1xk70soaF5y+cvaCB/NhyPBtBiCvFb3mfdgQEM8JpNwJCvijyXRq3hu6HfoVPr+GbvN1htVib9MYl/Y//lf3f8D0+dmxLkGZKUFP3GZDCngTFVGTJpSgVTmvJ69Ta1Dpz8lPnWss6B1gO0rpBz+abefwHpJ1EBkwYqq4Oawp2fQKsw2FD0I3w3bf5WM/O3KpNpr3oJbje8oAwnDbur7C4qhBBCiGtasmQJb7zxBunp6eV+bQ8PD6ZOncrIkSPL9DpDhgzh+++/58EHH6RJkyZ06dKFjh070qVLFxo0aFCic1ksFlasWEHfvn3RarUMGTKEN998kyNHjtCsWbMyegcVQwIzUdDVD4olJIC/f7EPzx/XnT9fSIWGjzPqng5sOnQrff76gz+sjcGSBYE9waMhWLLBkmN/VVuymeORTXB4Xd45p8yttujwIrYfX8T/AmFQ0dn3S8acrpTSpHFR3geAkw9YDHRukEXsVzpUbuE8PyeKz9ZaqB+k4fQlS+leO5/x30GHzdAm8l7e+XbfNXsphRBCCFF2pk+fXqHzmU6fPv2GArNVq1axbt06h21t27bl+++/L1DX29ubyMhI5s2bx/r16/nxxx/58ccfcXZ2Zvz48TxVgtFYf/31F/Hx8QwePBiAAQMG8O6777Jw4ULeeeedEr+PykwCM1GQWg1ubpCZqay//jrMmVPsw/MHZjExBfdnGd3YdOhWADYe7ssXy/qSnOnDC4M+xc05q/AmAW/roW0wjL0M6VaIMcPgi3CnG0zzg2bXmPTagUqt9I5ZjUrQpHUHzyZgTFF607xbgkttyDitBFYB3cFqgIwopZ5vW0g/BeYsCOyhJDBJOQzOAeDfDbJjITsGvFoq2zKjABW4RQA2yIlDpfcDtY5Pb7fy1Imj1G3QmNGjR7NixQomTZrEhx9+iNFo5KGHHuLHH3/Ex8eHnj17snz5coKCgrhUjKQs+cWmwKq9sGqviTu7DaT90weUQFEIIYQQ5WrSpElMmTKlwnrMJk6ceEPH9u7dm5dfftlh27UScHh5efHMM8/wzDPPkJiYyD///MOiRYuYNWsWPj4+3H///cW67rJly/D29qZr164A+Pj40LVrV1atWsWkSZNwd3e/ofdTGUlgJgqXG5QBfP01fPGF45DEa3AYylhIj9mBA47rT8/9AoAdJ7ux8qWhaDVF9xwNc4cDri6Mi1PxR7oSxK3IhFWZKsaENmZio54092+gZIHUeSlBk84LnPKta92VZ8kAzNnK82+qm5w5wrd93rKTF3jly2zpXi9fRRW4BOWtqdU0bKL0Xi1atAiTyYROp+O+++4jOTmZbt26MXnyZPz8/HB3d2fr1q20a9eOb7/9lvnz53Pbbbc5pJItjvkbYvjtQEeGPTad1t2H5f0shBBCCFHmRo4cWeZDCcuCm5sbderUKVbdb775htDQUAYNGgSAn58fQ4YMYfDgwdx7771s2bKlWIFZUlISf/75JyaTySEztdVqxWazsWLFCkaPHn1jb6gSksBMFK5zZyX/fS6tFi5fzpt8+hpq1VKqm83w22/QvDnMnAn9+sHBg/Drr4Uft3b/YMZGnmPu5yfROTuDJre4OLxGqLSsB37870de2/QasRmxWLExN+YYc2OO0b9+f57s8CSD6w5Gp9Fdu7Fal2L/SMqDTqe0t2nTpvZtjRo1si/37dsXUL5tmzRpElarFZPJxKlTp8jOzmb79u3XvcZnvwOc5sc1wznzTT1Urd6COveDuniBtxBCCCHEtezfv5+VK1fSr18/tNq8cEOlUuHm5oafn1+xzrNy5UpMJhNffPEF4eHh9u1Wq5VHHnmEhQsXSmAmaoAffoCrH6i8ehbpJk3gzjuViKtdO/D2BpUKjQaCgvKGMR45AgMGgM12/csuWB7Cv0dCeOQRZW4078KnOUMFPNz2Ye5tcS+f7/ycD7Z/QEpOCgDrT69n/en1+Lv6c1+L+7ivxX10qt0JTTUMPNRqNV99pSRPOXXqFL179yY4OJhdu3Zd99ioeDgXdYY6mWNh95Pg0xqCBqDy74faaizeL0wIIYQQ4ipPPfUU999/P48++ijjxo2jbt26xMXFsW7dOv777z9effXVYp1n2bJltG3b1v7FdH73338/n3/+Of/++y/t27cv5Oiq5ybHb4lqK1+PTZGOHYMPP4S+fcHXV3k2TaUClYo58cPpxnZA+XBfnM/4+ivPiJ04Aa+8AhERMH/+tY9x1bnyfz3+j3PPn2PmgJnU9a5r35eQlcDnuz6n2/fdCJoRxNhfx7Lg4ALOpZ7DVg2DjgYNGhAdHc3OnTt56aWXinXMY9/CgA9g78kMiN8OB9/AZXNX2p7sicuaMNg8CP59EQ5Pg5hVYCr/8fBCCCGEqFqaNm3KkiVL8PPzY/LkyQwYMIAJEyYQExPDwoULadiw4XXPcejQIU6cOFFkj9j999+Ps7MzCxcuLO3mVxiVrTp+Qq1ABw8eBKBly5YV3BJFVlYWR48epWnTpg6zpxdLKT57ZMCJJ/mS+YzBhFOB/SNHwvPPK2XPHsd9Eycq8V9xmmOxWvj91O/MPzCf5ceWY7AYCq1X26M2XUK70LpWa1oEtqBFYAvq+dSrNr1qRqORTZs20aRJE+rWrXvd+n1bO7NhUk4xzqwCtZMy1YBrKDjXArUe9P7KvG9OPsqwU7VeGXqq81SKkxe4hoPe9+bfnKh4K1bAhg0waRJZ/v43/jdG1Eg39f+SqJGq4j2Tk5PD2bNnqVu37jUTZIiyYbFYyMnJwdnZGU0xcyTcjGv9vksSG8hQRlG06Ggo5kOe16PHyHc8xnc8Zt/2Ap/wNY+TjSutW0P37rB7N5w+DW+9BT/9pNSbPl15tO2qRECF0qg1DGk0hCGNhpCSk0Lk0Uh+O/EbG85sIMOYYa93If0Cy44uY9nRZfZtzlpn9Bo9ThonmgU0w0PvQZYpi3CvcLz0Xng4eeCqc8VkNRHsHkwt91q46dzQqDXYbDbCvMLw1HvionVBo9agU+uu/4xbGXFycmLgQGXitHfffZfZs2fj7+/PoUOFT5z2x/4crHdEob6wHHPcdrKSzuJhiUZliL+qpk3JUGk1QNoxpZSEZ1NlSgS9n5Ltsvad4Fz8qRhEJZCcDMOGKcsnTsDy5RXZGiGEEKLaqHY9Zrt27WLMmDEFtv/www9069at0GNiYmKYOnUqu3fvxtnZmbvuuosXX3zxhiLsatVjBjBjRvEiotLwf/8HL7xgf5btyy8h/zQXs2bBM8/c2KkNZgNbz23lr+i/2HF+Bzsv7HQI1MqCChW+Lr6YrWbCvMLQqXW4O7njrHXGzcmNWm61yDZnE+4ZjpPGCY1ag6feE0+9J85aZxKzEmlZqyU6tQ6VSoWX3gsbNrz0XiTnJNPYrzFphjSyTFnU9qxtv67NZkNVSPfi5s2b6d27d5HtDQgIwMvLi+XLl5OTk0PTJk1wtcVD9gXIvgiX/4TE3WAzKVMFZJ3Pm6PtZug8wasFtPsU/Dvd/PlE2Vq6FO6+276alZlZ5b7JFhWrKvZ+iIpVFe8Z6TGrWNJjVkkcP36c8PBwfvnlF4ftXl5ehdY3mUw8+uij1K1bl4ULF3Lu3Dlee+019Ho9zz77bHk0uXJ76SVlnGGvXhAVVbbX+vBDpVzx5B13oJvwJuPntANUPPss7NoFTzwBdesqQxsNBuXxNp1OeUatqOGOeq2evvX60ree8vCoxWrhROIJDscf5lDcIQ7FHeJowlEupF0g1ZBaKm/Hho3E7EQAUuNK55xF0ag0OGmc8NR7km5MJ8QjBIvVQohHCB56Dw5cPoCrxhXfTr6YLphIj00Hq+M54uPjiY+PZ9jzwxj42EC8krxIN6ejUWuo612XUM++HMoKIj4rHi83L5rUaczKY8tJzUmhiVcI9V3c0GEmNjOBQCdXDOZMGrt6sD8pmlpacM+5SHBOFBosxFvARw3ZNqitTSM5bQf7T3enSdtX8QgdRLB7MIsOL6KeTz38Xf1pG9SW/y79hw0bHk4e1PGug8Fs4FTSKRr4NrAHrlmmLNQqNTqNDpPFhI+LDxfTL+KkccJoMVLbozaH4w9zIvEEnWt3xtfFF2etM2eSzxDmFYbRYsTdyZ2zyWep5V4LvUaP+spUCjZsqFVqe+BrtVmJSomirndd+75DcYcI9wrH3ckdtUqN0WJEhcqh57SowLnKGDeuolsghBBCVEvVLjA7ceIEDRs2JCAgoFj1161bx8WLF1myZAmenp40atSIxMREPvroIyZMmICTU8HnoWqcOnXg7Fll2WZTArT165W5za58C1AmVq1iHKvI/Rh4jjBe/uljbv1pOJZCbl0PD2jYUGliq1bKHNlarbKckqLsq1MH4uKga1cNIU5NiajblJHNHOcSsdlsZJoySclJQaPSkJSdRJohjTRDGjnmHDJNmcRnxpNhzCDTlInRYgQgJi2GLFMWWaYsjBYjOeYc4rPiMVqMJGQlYLVZMVvNZfKjstgsZJuzyTYrPVinkk4BcDblrGPFwVdelwKFj2rk1B+nmB0/G3yBW1FSYF7HuhtpdAFmWPcO8E6pnK00uTu5O/SwBrkHcSnj2pN869Q6TFaTfV2tUtt7OzUqDRabhf71+xOVEkW2KRuLzUJD34aoVWq2RG+hvk99gtyD8NB7EJ8ZT1xmHG5OboR6hpKak2rvPdVr9TTzb0a6MZ1sczahHqHoNDraBLVhX+w+At2UKS7q+9YnISuBqJQoe8Bbx6sOq06sool/E3xdfGlVqxWH4g6hVWvxcfYh3Csci83C5YzL1PasjQoVqpSUYv3MLFYLVpu1wobzCiGEEFVNtQvMjh8/TpcuXYpdf8+ePTRv3hxPz7y87F26dCEjI4Njx445TGYnULqk6tZVctk//njedpsN/vsPPv4YruqtLC3hnGcx9zpse5EZfM3jZOFGejrs3ats37eveOfU65UElFlZ0KIF1K4NHh4q2rZ1x9PTnTp1oEFEME5OpZMLJdOYSZohDYPFQHxmPMk5yRgtRowWI0nZSWSbsjFZTaTkpHAm+Qyeek+yTFmkGdKwYUOj0pCYncixhGMEugVyPOE4BosBtUqNj7MPmaZMcsw5qFBh4xqjlHsAR4Gi5vLef+W1DuAO+ADVIy/KDbl62Ov1gjLAISgDsNqsJOckA0ogDcrUDvldTL9oXz6ZdJKTSScLnPdI/JEC23478dt121NaQl6EFGfIuvKdVdCX9cEKl367hE6tI8g9CE+9J0cTjmK1WVGr1HQP687RhKPoNXr8Xf2x2Cw0D2hOtjmbdEM6HnoParnVoktoF9adXkeQWxAatYYOIR3YfWE3RxKO0DaoLS0DW9LAtwEnEk8Q7hVOjjmH9iHt+SfmH3RqHfV86hHoFoibk5v9vCpUqFVqVCoViVmJ+Lr42ns4k7KT0Gv0uDm5AdiztVbpHk0hhBBVVrUKzGw2GydPniQgIIDhw4dz+fJlGjVqxAsvvFBkgHXp0iWCgoIctgVemUT54sWLEpgVl0oFbdvCzz8rBZQJqT/+WCll5BNe4hPyUsN/xRNM41ViCC3W8QaDEk+CksegKDqdkoCkVi3l1d8fAgKU4uenTOHm46OU3GUvL6XHLj83Jzf7h8AI74hiv8+SyP1wabKaSDekk5yTjIeTB5czL6NT68g2Z2O1WYkdH0uWNYtRHUYVfbIflZfQlqE8PONhLp67iEuwC4fiD9EuqB0pOSnsvrgblUpFPZ96eOo9iTwaSRP/JmSbsmkb3JafDvxEU/+muDm54aRxQqfW2Z+L239pP3W865BmSGNv7F6cVWCxQVcX+Cvf42ttAxqzL/64fT3cK5zzqeftvUYqlQpnrTPJ2cn4uviSbc4mJSeFut51HXoNfV18ScpOKs0fd41z8aq5BS9l5gWpJquJ82nnHfZbbVa2nttqX7+QfgGAQ3EFu2y/2ftNkde9OogtCVedK1mmLIdtucNbC6vjpnPDx8WH1rVak5KTgg0bKTkpDGowCG9nb/6M+pMI7whqudWikV8jftz/I04aJ3qE9aBDSAcuZVzicPxhOoZ0JNwrnPq+9dkStYXWQa3RqrXU8apDpikTtUqNm075e6BSqbDZbBgtRvRaZe4Qm83G2ZSzhHmGSc+jEFVUNUvlIIpQWr/nKhWYxcTE0KdPnyL3L1y4kKysLIxGI2+88QYqlYp58+bxwAMPEBkZSYMGDQock5OT49BbBqC/MqGWwVB4qvXrsdlsZGVlXb9iOcjOznZ4LVceHvD220oBSE5G+/XXOE2dWmaXfIKveIKv7Os5br5Edv+Iky3vYu9JT9LTVdhscOyYCi8vSExUkZCgQqOxYbEU/S25yQQXLiilJDw8bHh72/DyAh+fvGVvb9uVdWW7UvLqeHsXDOpuhAsuuDi7XGmLh8O+Zt7KBOITJkxgzpw51zxPzMEYZt87m+TkZF566SVe6vUSUSejGDNmDDqd4wfGr/p/5bD+9YCvi91edeI/OO19HHXmqav2HMfmo8Hm3girV2usXq2w1OqLzbN5sc+d39XPeeWuW6wWe5ZNGzb7ulqlJtuUjV6rJ8ecg7PWmRxzDjabDYvNgsVmwUnthNFqJCUnBTedGzZsGMwGtGotVpsVk9WEWqUm1ZCKq86VlJwUXLQuZJmy0Kg1ZBgzUKlUqFCRkpOCSqXiYvpFgt2DyTJncS5NmX/Pw8kDF60LOy/uxEvvRYYxg/o+9fkv7j/7h/wMYwbeem9SDCmkGdK4nHmZet71SMpJYsu5Lfg4+2C2mmlTqw1bz+cFTW1qteG/y//Z19117mSYyjZJTnm4OigDHIKyq+tkmjLJNGUSkxbjUKew3sr8biZ4DPcM51zaOfu6j7OPvYcVlCk+bDYbFzMuEu4Zjl6jtycLyjRmUsutFgnZCQyuP5gUQwpxmXH4OPvg4+xDq8BWrD+7XgkkfRvRIqAF++P2k2XKonVga+r71Eev0ROfFU+QexCqK+OXVSoVJovJISjM/Tm56m48AUOF/r8kqqSqeM9YLBZsNhvp6enyWEwFyA2UbDYbFktRw4NKT0ZGBlarFZPJhNXq+BB/SZ4tr1JZGU0mE+fOnStyf0REBFlZWbi6utozsFitVm6//XY6duzI27kBQj4TJkzA2dmZmTNn2rdlZ2fTpk0bZs+eTb9+/UrUxoMHD2I0Gq9fUYDFgsfevdSaPx+vHTvK9dJJ/fqRNHAg6c1bkuXqg06v4tQpF4xGNQkJOs6edSY7W010tDMpKVoyMjQkJupISdFeM4ArTW5uFry8zHh4KK+ennnLRb26u1twcbFSkgREGRkZrFq1Cj8/P1599dUStXHs2LEcOnQIPz8/3n77bWJiYggJCbmpDFQqm4nA5AUEJv2CkyXhmnVT3Hpw0f8Jsp0b3/D1xPXl/qditpppM+R21IkJGDWQGOLHuUXLMdvMJBmUnsgMcwYalQaDxUCiIRGrzWpPlpJpziTHkkOOJQdPnScn007irnMnx5JDoiGRIJcgEg2JpBpTOZl2kqZeTVGr1Gy6tAkntRMqVDT3bs7epL32ttXzqMeZ9DOFtjs3WBYl5651J8Oc4bCu1+hJNCgJjbQqLWFuYZzNOIuXzgs/Zz+c1E609W1Luikdi82Cu9adULdQarvW5lDyIfz0fvjofWjg0QCzzczFrIvU86iHv94fV60rsdmxhLiEOHyAMVvNaFQaGV4qqiytVouXlxdubm7lkh1QlC+bzYbBYCAhIQGTyVRkPScnp+qXlVGn01G/fv1r1rm6V0CtVtOgQQMuX75caP2goCBOXDWGLS4uDoBaV9K230g7C+udqwjZ2dlERUURERGBi4tLRTenoBYtYOxY7N9Vm82oTp5EO28e2q+/RnWDvZbX47thA74bNtjXbYGBtG/aFFtYGNaICGxt62CrVQtbWBi22rWVTCKYsFpNJCdDQoLKXlJSICVFRUqKitRUSE7OW05JUV1ZB6OxZB8sMjM1ZGbe2B9xd3cbHh42PD1zl8HT0/HV3V3Z7+Fho0uXLri7WwkL+4Hz5ws+11SUefPm2ZfPnDnD6dOnadGiBfPmzWPdunXce++9N/jvqBVm2zQsqfvRJGxDnXoAdeoBVOlHUdnyEqh4Z27DK3sXphYfYK47vlQnRReFc7LaUFlBZwXXTBVuDZoSFRVFy4YtK9XfmNxgMrfHx2A2YLAYSDOkoVapsdqspBuVob5atfJfYZohjYTsBCxWCyqVCr1Gz4G4A7jqXEk3ppNjzkGr1hKXGYfFZuFA3AF6hfdid+xuzqefJyErgfZB7ckx53A44bC9LU38mnAsMW/OP39XfxKyrv2lQ0XLH5TlruffZraZOZuhDBNONaWSalIyzx5LLeHchkVw1jqjVWntPbbB7sH4ufhhsVlIM6ThrffGS+9Fq8BWJGQn4KpzxWw10yGoA/6u/uy8uJN63vUIcguiTa02pBhSyDZlE+IRQoh7iD1z69VDRKt81tRqqtJ/limCzWYjIyOD5ORkMjMzK7o5NUpuT5lGUz5f7Hh7e+Pn51fotU6dunoUUNGqVGB2PX/++SfPP/88a9euJTg4GACz2cyxY8fo379/ocd07NiR5cuXk5GRgbu7OwB///03bm5uNGnS5IbaoVKpKt08Gy4uLpWuTUVq314pn32Wt81qhcRE+OcfWLlSeY6tFIc0qOLi0FwJyAvl6alkBqldG/eQEMJCQiC3tLnyGhSkZBMpQna2kh0yOdmxJCXlveZfzr+tpL3wGRkqMjJUxMaW7DglXeNCIBB4oURHnj59GoBDhw7Rrl07AObOXUerVn2IidnPM8/MJji4Fs7O4Oys/KiuXtbrQa3Od1K3bhCSb/5BiwFSj0Dcn3DsU8g6j8pqxOnAizhlHoaOX4NavpEsUzk59kVVTo79Q1Jl/xvjitK2IIKuU7P8WG3KcBez1YxWrbUnI4nLjMOGDRUqLmdeJs2QZs/qmmnMxIaN6JRovJyVoazRKdE4aZw4l3aO2h5K9szfT/+Ok8aJyxmXGdBgAD8d+MmeDfSW8Fscnvtz0bpgw2YPPMsqe2xJ5JhzHNZjM2KJzcj7g5b7rOKOC46jLX45fGPJpxr5NbJn1vXUexLhHUGYZxgGi4FLGZdo6NsQL2cvOgR3IDo1mgjvCGw2G438GuHr4suBywdoHdQajUpDq1qtuJRxCRedi/0ZWFE6KvvfmcK4ubkRGBiIxWLBbK74f1s1RXZ2NmfOnCE8PLzMg3mdTnfN3tCS/A2oUkMZrycjI4M777yTkJAQXnnlFbRaLf/73//YunUra9aswd/fH6PRSGpqKl5eXjg5OWEwGLj99tupU6cOL7/8MjExMbz22muMGTOGp59+usRtqHYTTFclNpuSzSM+HmJj4dgxiI6G8+fh5EnYswcyyvh5GT+/vIAtt9SqlZc1JDeDiI/PVRHItd9WRkbRQVv+5bQ0paSnOy6X/F+5AWiLkrqxtHQB4gE3YANwFqgHOE5t4eRUeNCWu6zXK8lYdForTlmH0GUfwUlrRKcxofOOwCnsNnROapycrtTTYV++kW1abeFFoym4rUZ8/tLpIPfDhU5HVkpKzfkbU83l9hYZLUb7dA8ZxgzSDGmYrWbMVjOJWYkk5yRjtprRqXWkG9M5lXQKs9VsnxJi67mt1PepT3JOMtGp0dT3qc/yY8vJMeeQnJNM+6D2/HvpX/t1XbQuGC1Ge7bS6qhjSEcOXD6An6sfrjpXmvo3ZeeFnRgtRiK8Iwj3Cqeedz3SDGlkmjJp4t+Eej71aOjbkHRjOjq1Di9nL2p71MZZ68yuC7voWLsjAN7O3mQaM3HRudjnXaxuatRnGVEqKtM9U5LYoFoFZgDnz59n+vTp7Ny5E4PBQPv27fm///s/GjVqBMDOnTsZO3Ys8+bNo3PnzgBER0fz9ttvs2fPHry8vBg5ciTPPPMM6mJ+cM5PArMqymxWAriUFGXOtpgYJbiLjs7L+nHhgkNvwU3RapWUjvlTPeZfrlVLSf3o66sUL69iB3JXs1ohM9MxWCssgMstqalmLl9OR6VyISMjlbS0Cxw92hFQA6X9bV848CUQCTwBtALSAb9Svk75UKuLF8CVJNgrTj2NpmBRqwvffr1yzeNUVm7p5fit4M4dGUSfj6Jhw3q4ubnc0PVEzVLU/0u5H0dyzDnoNDqMFiPxmfGkG9NJN6SjUimJcWLTY+2TyWvUGraf304DnwZEpUSRakglwDWAufvn4qH3IC4zjlvr3Mpf0X/Zr+Ol9yLHnIPBUjZD5StaPZ96NPVvyuqTq2ng2wBPvSftgtqx6PAi9Fo99X3q09i/MT7OPhyOP0yIRwhN/ZvSulZrMk2ZuGhdCPYIRq1SE+EdgcVq4XjicRr7NcZF54KrzpWYtBild7acvo2SzzKipCrTPVOjA7OKJoFZNWazKYFbbCxcvFh4yd1X2glg1Gqlly03UPP1VXrn8q/nFm9vZeillxdXHjIr0affwu6Z3GGKx44dY+TIkbRr146oqGguXixhmsrr0Gh8sFhS8fd/E6MxBYsFdLpJZGXtwGRqiM3WAiVwc0cJFEV5ciabbBz/jriRQRZuN3XeGw0irxVIFra9smyrqHZUlh7dyvL/ksliQqvWkpidiE6tIzE7kdScVNIMaaQaUtGoNMRnxXM54zJJ2Um4OblhtBg5mnCUANcAjiYcRafWYbFZ+DPqT/t56/vU53Ty6Qp7X+Ut3CscT70nh+IO4e2sPPvXI7wHPx/8mVDPUDz1njQPaM651HNczryMt7M3zQOa09C3ITtidhDiEUKrwFY09GtIoFugMgxU62LvITRYDCSmJZJ2MY0GDRsQ5hfG76d+p6l/U/xd/fHQe3A54zJWm5Vgj+CK/nGISqKy/J0BCcwqlARmAptNGVeYG6zFxSlzuhX2Ghen5OIvax4eeYFa/qDt6nV3dwxaLeeTkght3BhnPz8l8Um+kqPRoHdxYfv27cyePZuxY8fyzDPPcObMGRo1alQgmU5ZCAoKxsvLh5SUFJo0aYXJZMaSlUCo7jzpOTb0Wi1uQZ3I0dfHxcUbJycPVConQHelaLFatYAOq1UHOGGz5RWLRYfN5oTV6gQor1arFqtVZ3+1WLRXihqLRel0vV4pqt5VmXUrLW+SScbXYVtDTnCKBozjGwaxlif5kkvIh6PKqjIEpjabmbS0FPz8vNHrtbi6Kn+Ccou3tzJgICICgoOVY6oim82G2Wq2P78XnxmPVq0lOjXa/izhxfSL5JhzuJx5mSD3IHZd2IXZasZoMWLDhs1mIzYjlqTsJC6mX6S2R21OJ5+2P58oCtchpAN7Lu6xrw9tPJS9sXuJSYshwDWATrU74e/qz9z9c2ns15gWgS3wc/HDSeNEXFYcjf0ak25Ip2+9vlhsFo4nHKeWey08nDxo7N+YjWc2EugWSC33WrQJakOmMZP4rHjCvcLRqXW4ObmhQkVUShThXuHKlCoaJ84mn8XXxRdPvae9t1ESzpSNyvT5VwKzCiSBmSiR3F64q4O2y5fzHhxLTMxbTkpS6lc0Z2eHYC1Fr+e0Wk3LwECeOXGCdJuNQRERjN28mQgvL1CpiKoM7S4DarUarVaLTqdDq9U6LBd3m0ajRavVodFo0WiUV7VaWc59VamUbWp17rLymrusVivLoAE0qFQa+/LVxWbLXdba13O35X+9urhmJDJxfjuHs83qs4Ls9HTe2vUAGpR+zNF3GzHadFgsFKtYrcWrd71jq0qAK4pPp4PmzaF7dxg2DHr3luGvV7PZbJisJuIy41ChwmQ1kZqTSmxGLE4aJ84kn8HH2YeErAROJZ0i05Rpf7bt+33f4+fqR0xaDLdF3Mb60+ux2qzU9anLobhDNPBtcN35+0RBKlTYuPbHaxetC04aJ1INqfZtdzW5i1+P/QpAXe+6hHmFkZqTyunk0+g1eloHtSbUMxSdWseJxBN0qt2J1JxUuoV143jicTKNmXjqPfF39SfYI5gd53fQ1L8pfq5+NPZrTI45B71WjwoVbk5u+Lr4km5IB8DNyQ03nRvOWmcOxx+mkV8jjBYj7k7uZJmy0Kl1DllMbTYbVpsVTb6EW5UpyKxMn38lMKtAEpiJMmc2K8FZ/mAtfwCXmpr/gbGCy+WYsvcc4AGkAXOAe4DfgOXAq8CbQBwwAfgIJQ1Ic2Ad4APYgJSrzumpUpF25c+WTqXCJH/CKh2NRuNQlOBTU6KiVqtRq9UOy/lLUdtVKjVqtQaVSu1QIG87OG5XqRy3K+FlwX02m+P23OX825Wg1nF73qvjvryicdhmteYuq7BaVVfWHV9zt1utqivH5N/uuM1iyTvWYrl6u2Mdi0U51mLJq5v/Vdmu1AXVlfesulLKXosW8Omn0LdvuVxOFMJms5FlyiLbnI3BbECv1ZNmSON86nlcda7EZSoZjs+mnMVN58bZlLP4OPvgqnNl5YmVeOm9lCQwwe2Zf2A+nnpPolOi6RDSgUxTJvGZ8WSaMskwZuCqcy0wtYReo6+2zwdWFY38GnEiURkd4+HkQYBbABfSLqBSqVChwkPvQY/wHhjMBn4/9TvtQ9oT7B5Ms4BmLD+2nLbBbTFZTHQI6cCFtAskZCcQ4RWBVq2lS2gXTFYTW6K2EOYVRoBrAE38m3Ak/ggqlYpabrXQa/WEeoZispg4GHeQJv5NcNO5Uduztj0YTc9MJzUmtVJ8/pXArAJJYCYqPbPZMetHbsCWmgoZGRiTk4mPiiLQ3R2d0agEcsUpN/inxIbykS4VcAJcUHI3+l/ZFwuEAFkoAV4wkHTlWG/gFEoQBxB1ZTkNJaBzvvKaCZgAI0r6EtNVr8aiikqFUaPBqFZjvlJMajVmlQqTSoX5Ssldzj2fGTDZbJitVkxWK2arFbPFgslslnTJotpSqVRXguO8VyUAVl21rMZqtaLV6uzrNpsS5NlsKnvwabGoMJvVmEz5A0DltVYtNX5++a9T8NpFvZZWKe3zybWLLkajkQsXLhAeHo6zszMqlcqeJEatUpNlziLLnIWTxgmT1YSz1pnotGhcda6kGFJw1bkSmxGL2aZkD802Z+Oud+d82nlAmX6hkX8jErIS2B+3HzedG65Orrjr3UnNSWXf5X246FzINmfTPqQ9/8ZeySqqgtqete3TN9i/nyjua1HL5bmtcnRwlYmRdUYy9565Ff75VwKzCiSBmajqbuiesdmUjJVZWUqQlp2tlJycvNf8y8XZd636ua/l8XxeKbNptVgCAjD7+2P288Pk54fZ1xdzUBCmoCDlNTAQs7e3EsyZTJjNZvtr/uXCtlkslmuW4tQpsm5cHJa//sIC9mKKiMAaFeWwzRIYiCU4GMv+/XnbAgIwu7oW67pWqxWrjEsUQojyVxHB4fW23eh5QmH/r/tpVacVFakksUG1mmBaCFFBVCpwcVGKn1/5XddiyQviCgvqEk/AgRmQcDSvyyz31eoC2hBQB4LaD6weYDQXP2C8we+0VGYz2thYtNeb/Vuvhzp1oEkTaNoUmjVTSpMmSqbNirBhA/Tv77DJ1Lcvum+/dawXFwfz5sHAgXnb4uOVn9mJE9C4sbLt669h/PhCL2WzKYkPcoO0/CV/8Fac7TdyTGmeq6THWCwW+3vP/3O41mtp1SmPa1osFgwGA1qt8hGkJOfLyrKRk2NF6U+34upqw2Yr+pry3bMQJWS76rUqi4dzB89VeGBWEhKYCSGqLo0mLwlJoW6BOx+B6EVwdDok7823Lxs4faUAKg14t4KAHhB4CwTcAi5BhZ/WZlN667KzlV7CwiaEu7qkpEBCghKgxMUpr9fq8TMYlCDmxAlYudJxX5Mm0KmTUjp3hlatlFmxy1ohE7QXCMpy5Q/Kcu3cCV265K0//jh07QoNGyrZHfbuhZYtYe9eVFqtfdiUqF5uZiSHzQajR8OCBcr6bbfBb78VXTc7GzIzbej1Vlxc8oK40ir5A8DyLBV13Yq6tsFgIC4uDn9/f7RabZV/38r9WfT6zWwr7+Mq87maNWtmn7O4qpDATAhRvalUEDFKKUl7IfZ3iP8bEnaAMSmvns0CyfuUcuJzZZtHQyVA8+sAHo2UdddQUKmVQMjJScnvHXwD6eFtNuW5vvh4JQvn+fPKhObnzimv0dHKZOdZWQWPPXZMKfPmKevOztCjB/Tpo2RFaNu2bHKM32zimPxBWa5WV32TefCgkoovPR0GD4atW5XtH34IEydWnsm4RIVQqWDOHPjrL7hwAVavVr63GDpU2X/pEixbBitWKHF+YiIoY5o0+PpC69bQpo0S0PXqpcwkIio/eSxDlFTuPeNW5Be3lZMEZkKImsO3nVJACYzST0DC33kl5RAO4zfSTyrlzPd529R6cK0NLrWVIM2+fOVVHwB6X9B5g/oawZFKpUzY5O2t9BgVxmpVArYjR+DoUeX1wAH47z/H3racHPjjD6VMnqxMRj5gANx1FwwaVHqfPgvpMSszV7f5//5PKVdbtAjuuad82iQqBU9PmDEDRo1S1u+5R+lFi4qCP/8setqEpCTYvFkpn34KWi1066b8U+nfH9q1k1T8QoiKJYGZEKJmUqnAs7FS6j2kbDOmQPwOiN+qlMRdYL1quKHVABlnlHI9Om8lSHO6UvS+4OQDWg/QuoLWDTRXXrWuoHHL2652ApUWfLTQoxHc2lxZV2vBZIWDR2HXPti9B7ZuUwK4XMnJsHChUvR6pRdtxAileHre+M+sHKdaKLZ771VKfiEhEBmpDPWUHrZq6Z57lA7jNWuUUb/ff1+wTlAQNGqkPJKZmQnHjys9arnMZqXn7a+/4LXXlMdje/RQRtO2aKFMcB0aqpynqk5yLYSoWiQwE0KIXE7eUHuwUgDM2ZC0B9KOQtoJpfcs4xRkXQBT6jVPBYApRSkUI4i7EYHAEGAwcFkNh1VwCDhsUeYIAOVT6+rVSpnwKHRxh97e0M5L6TJQaZSAT6VRgr786yqt0uuXu37kaNm8j9J28WLBYZO9e8NHH0H79hXTJlGqVCpYuhQefBCWLMnb3qCBEqfffbcySvbquDwuDv7+G9avV8qpU3n7EhOVIZArVjgeo9Eoo5WDgiAwEGrVUkphy35+EsQJIW6cBGZCCFEUrYuSCCTwloL7zJlKgJZ98crrBeXVmKQUQ1LesjEJbGWY/l0FBFkhCOiDMpHaMWA38C+QfKWe0QZ/pSvF+zz0BHqjTBpXHJdLud3ladMm6NAhb33iRHjpJeXTtKiSXFyUkayTJys9YbVrK71d1+okDQyEO+9UCsCZM3lB2saNSp6eq1ksEBOjlOtRq5V21K2rlHr1oHlz5bm2evWkA1cIcW0SmAkhxI3QuoFnI6Vcj80KpvS8IM2UAZYsJbgzZ4Hlyqs5M2+71QQ2s/JqNSvLV6/b65jBZlISmNgsynpXC3Qxg8UMx7NhSyZsy4aMK8/QpQArgJVAe6Af0JxrTzZqyP/+UQLAqmr6dKXk2rhR6VUTVYpKpeS6uVH16sGECUqxWpV8O4cOKY90xsQoI4RjYpREI/HxRT+/liv3sdDz55Uhkvl5eytJR/r2VZ5rq1//xtsthKieJDATQoiyplKDk5dSqFsxbXgFMBqVh3LmzlVyjJvNSq6TPVdK40bw3DPwwD2g110J+CxXgkIL/PYC8Ktyvndegldn2E9vHj8M7Z//wonzBa9dFfTpo7z6ecGyGdCkwZWhnVolCHfyBb0faPQV205RZtRqJViqXz+vRy0/i0VJIHL5slLi4gouX7qkJFRNSCh4fEoK/PqrUkCZzm/wYKUj189PSe0fF5dX1GrlcckuXaBjR6WHUAhRvUlgJoQQNYWTEwwbppSLF+Gbb+B//1OWAY6fgCefgbffheeegyeeAO+AvOMN+brTxjwP6/bAli1kNWoEn/6M9tw5ZTLs/Jo0UVL7VxWJqdDrMWV5EDAKx/8pte7gXv9K4pgm4NUM/DqDWx0Zp1bNaTQQEKCUFi2uXTc9Xel9O3UK9u+Hfftgx47c9P2K48eVUhxarTLl3x13wO23K/+s5HYTovpR2XJnZROl4uDBgwC0bNmygluikLk/REnJPVPDmExKtoPZs2HLFsd97u7KJNDPPQdhYY6fBJOTwdubrNhYjl64QNNmzXB1cSmYbzwlRRnDVZXVBV4FrvXPwTkI/LuCfxfl1beD8oyiKKCm/o2xWpUgbf16JRfP9u3XHxpZlPr1lQCtXz/luxAfH2WGCa1WmQnEalVe8y/nvmZmKv98k5OVf57mK0OS1WrlHN7eyvSM3t7KemUIAGvqPSNuXGW6Z0oSG0iPmRBC1GQ6HYwcqZTdu5XnrpYtUz7FZWQoE0bNnKnkEc8vd9JOL6+8HjeVCr79Fh670uP077/K/nvugcWL844dNUpJ5V9VnAXGAS084c0AMEUrwzvzy7kEMb8qBa5MddAafDuCb3tlknKv5qDWlXfrRSWhVivPw7Vtq0zJl5SkBGdHjyrBkl6fl+ExIED5J3j8uFLnr7/g5Mm8c50+DZ99ppSypNMpwyxzi79/3rK3tzK88lrF2TnvNXdZq60cwZ4QlZH0mJUy6TETVZ3cM4JTp5SA7IcflHT7hbnyX0eh94vFonwNr7/yPFZCgvJJE5Sv9/ftU3rnBgwo4zdSRqa9C0+NhLRjkLRPmZw8cef1p1BQ68G7Ffi0Ao/GecMh3evWqIBN/sbcmJMnlUdDf/tNCdTMVTT5jlrtGKi5uipBno9P3mtuCQxUnrPz8ckmPf04nTs3ws1N7hlxfZXp74z0mAkhhLhxDRrAV1/BW2/Bl18qJX82gxkzijwUUB7GyT+Zk7+/EshZLHnb+/eHqVNhyhRlfelSZY6xuhWUHKUkXn1dKZcvQ6srWSJsViVQS/gbEv5RXlOPoGRXucJqgKTdSslPpQWP+kqQllt82oBXS2UeOSGAhg3hhReUkpICGzbAnj3Ks2zp6Uqqf4tFCXxUKqXkLufflj8Q8vJSAiRQjk1LU86dkqIMdUxMzCvZ2aXzPqxWyMpSSvG5AG3Q621ERECzZnmlUyflT5YQ1YH0mJUy6TETVZ3cM6KA7Gzo1Qt27VLWz5+H0FCgFO4Xm81xXNOWLcq1qooffoCHHip8nykdkvdB4h5I+leZrDz9RPHPrfWAoN4QOgzChoPOszRaXOHkb0zVlJXlGKilpip/GooqOTl5r1cv51/PyFACwpv5NFqvntIBP3o0dOsmQyVF5fo7Iz1mQgghSo+LC+zcqfSaaTTKV+2l5epPUD17Kl+pp6YqX+erVMrYrTvuKL1rlqaHH4ZVq5Tn8q6m84DAW5WSy5QGacfzSnru6wmw5Dgeb06HmBVK2fMM1B0DzV8F19CyfU9CFMLVVSlhYaV/7vy9dblJSZKSlOkHYmPh3Dkzp05lkprqwdmzanKu+qdy5ozSyf/VV0oP2scfwy23lH47hShrEpgJIYQoHn//8rmOSuWYyfH225VPbTNmwLZtSrbIFSvKpy3FERmp5E8/ePD6X9XrPMGvo1Lys1kh85wyHDL1sPLMWtwWyIlT9psz4ORXcGYuNHsFmk9W5lkTohrI/b7Hx6fw0cxZWUaOHj1F06ZN0etdiYqCI0fgwAFlbvht25QEs6B07N96q5Jg5d13lWQjQlQVcrsKIYSo/Dw8lGfe8tu6VfkEVhkcPgyensrDPjdCpQb3CKWEDFS2WS1KgHZ2HkT9BOZMsGTBwTcg/i/osRicSrH3UogqQKPJmwj8jjvgtdeU4ZCRkUpS2UOHlHoffghRUfDzz46PvApRmamvX0UIIYSohG65JS+pSHIyzJ1bse3JyIChQ0vvfGoNBHSDTnPgznPQ+DklUQjApT9gfVcwJF77HELUAO7uMHYs/Pef0rGe20u2aBE8/3xFtkyIkpHATAghRNWmVitDH8eOVQK1ixcr7gGTVauUr+1Lm94X2s+EPptBf2XqgbTjsH0UWKto3nQhSplGAy++qIx0zg3OZs+G33+v2HYJUVwSmAkhhKhegoOViZ5ye9O+/758rz9pkvIATFkI7AEDdoJzoLJ+6Q/Y/2rZXEuIKmrwYCUgyzV+vNKhLURlJ4GZEEKI6kutVjIn2mxKApHHHy+f6zZvnpeNoLS514UeS/OGNR6bcWXONCFErvHjoU8fZfn8efjf/yq2PUIUhwRmQgghagYPD5gzJy9Iu+eesr3eAw+U3bkDb4GWbynLNiscmFJ21xKiClKpYNasvPVPPgGjseLaI0RxSGAmhBCi5vHwUDID2GxKRsWysHixMhNvWWnyArgEK8vnIyFxd9ldS4gqqFmzvHw8Fy7AggUV2x4hrkcCMyGEEDVbs2ZKgGY0wiuvlO65y7JXTusKLfL1lB18u+yuJUQVNWlS3vLPP1dcO4QoDgnMhBBCCACdDt5/XwnStmwpnXNu2qRMplRW6j0KruHKcuzvkH257K4lRBXUrRtERCjLmzaVbSe2EDdLAjMhhBDiarfeqgRox47d/Lnq1r35cxRF4wR1rzzLZrPAucVldy0hqiCVCkaOVJYtFli5smLbI8S1SGAmhBBCFKVxYyVA27//5s4TH1867SlMnfvzlqNkrJYQVxsxIm956dKKa4cQ1yOBmRBCCHE9rVopAdqNZg947rnSbU9+3s3Bu7WynLgT0k+V3bWEqII6dVKmNwRllLJZ5mQXlZQEZkIIIURxjRoFBgOEh5fsuAULwGotmzYBROTvNZPUc0Lkp1Yro5MBMjNvvgNciLIigZkQQghREk5OEB1d8jFRkZFl0x6AOqPyli+tK7vrCFFFdeuWt7xjR8W1Q4hrkcBMCCGEuBEjRsC5c8Wvf/fdZdcWt3DwaKgsJ+4Cc2bZXUuIKqh797zl7dsrrh1CXIsEZkIIIcSNCguD9PTi1z9/vuzaEthLebWaIF66BITIr3VrcHNTliUwE5WVBGZCCCHEzXB3h7S04tX98suya0et2/KW4/4su+sIUQVptdC5s7IcE6MUISobCcyEEEKIm+XhAcnJ16/3wQdl14ZavfKWL28uu+sIUUV16JC3fPBgxbVDiKJIYCaEEEKUBm9v+Pff69eLjS2b67sEg2cTZTlxN5gyyuY6QlRRLVrkLR86VHHtEKIoEpgJIYQQpaVdO3jggWvXefLJsrt+7nNmNjPEbyu76whRBTVvnrd8+HDFtUOIokhgJoQQQpSmuXOvvX/58rK7dv7hjIk7y+46QlRBTZqASqUsS2AmKiMJzIQQQojSpFbDqVPXrvPPP2Vzbd98D9Ek7S2bawhRRbm6Qr16yvKRI2U757sQN0ICMyGEEKK01a8P/v5F73/33bK5rns90Hkpy8kSmAlxtdznzLKyICqqQpsiRAESmAkhhBBl4cyZovetXl0211SpwKetspwVAzlxZXMdIaooec5MVGYSmAkhhBBlwcMDPvqo6P3795fNdX3b5S0n7SubawhRReUPzI4erbh2CFGYahWYRUZG0rhx40LL2LFjizxu9uzZhR5jNpvLsfVCCCGqnZdeKnpfv35lc02ffIGZDGcUwkHDhnnL1+rUFqIiaCu6AaVp8ODB3HLLLQ7btm/fzuTJkxk3blyRxx0/fpw777yTiRMnOmzXaqvVj0cIIUR5U6uVWW337Cm4Lz4eMjPBza10r+nQYyaBmRD55Sb/ADh9uuLaIURhqlWPmbOzMwEBAfbi5OTE9OnTefTRRwsEbPmdOHGCZs2aORwbEBBQji0XQghRbW3cWPS+N98s/et5NAKNq7IsPWZCOPD1BU9PZVl6zERlU60Cs6vNnj0bvV7PU089VWSd7Oxszp07R4MGDcqxZUIIIWoMT0947LHC982YUfrXU2vAp42ynHEGjMmlfw0hqiiVSkmaChAdDfLUiqhMqm1gdvnyZRYsWMBTTz2Fi4tLkfVOnjyJ1Wrl999/p3///vTq1YtJkyYRFyeZrIQQQpSSjz8uet+WLaV/vfzDGZP/K/3zC1GF5Q5ntFjg3LmKbYsQ+VWph6hiYmLo06dPkfu3bdtmH4L4yy+/4O/vz9ChQ695zpMnTwLg4eHBrFmzSEhI4JNPPmHs2LH8+uuv1wzqimKz2cjKyirxcWUhOzvb4VWI65F7RpSE3C/FpNPhotWiKuzr+V69yMrIUL7KLyUa1yborywb4/7F7NG51M59s+SeESVV2vdMWJgO0AFw5EgOQUEy03R1U5n+zthsNlTF/PtepQKzWrVqsWbNmiL3+/r62pdXrFjB8OHD0el01zzniBEj6Nu3L15eXvZtDRs2pGfPnmzevJnBgweXuJ0mk4mjlSwHa5TMoihKSO4ZURJyv1yfdvVqWg8YUOi+5Jdf5lJRwx1vgGu2O02vLKed20a08bZSO3dpkXtGlFRp3TMuLv5AHQB27LhMcHBCqZxXVD6V5e+Mk5NTsepVqcBMp9NRP3dg8DUcOnSI2NhYhgwZUqzz5g/KQAkAvb29uXTp0g23s7I8s5adnU1UVBQRERE31Psnah65Z0RJyP1SMpZ+/dBs2FBge+05c/CZPBn8/UvnQuYwbOdUqLDho4rBtWnT6x9TTuSeESVV2vdM1655T/Lk5ITQtKkkfKtuKtPfmVOnThW7bpUKzIrr33//JSAgoFhB3IwZM9i4cSOrV6+2dzPGxMSQnJx8w8GVSqXC1dX1ho4tKy4uLpWuTaJyk3tGlITcL8W0eDH4+BS6y7VOHbBaS2lIoyu414eMU2jSj+DqrFeSglQics+Ikiqteyb/JNPnzulwdb326CpRdVWGvzPFHcYI1TT5x7Fjx2jUqFGh+4xGI/Hx8RiNRgAGDhzI+fPnmTp1KmfPnmX37t0888wztGvX7pop9oUQQogS8/aGV18ter9aDSZT6VzLp5XyasmGDJmwSYhcYWGgufI9xdmzFdsWIfKrloFZQkIC3t7ehe7bt28fPXr0YN++fQA0b96cb7/9lqNHjzJ8+HCefvppmjZtypw5c0oU4QohhBDF8t57197v5AQpKTd/Ha+WecspB2/+fEJUEzodhIYqy5XkESQhgGo6lPGbb74pcl/nzp05fvx4gW0LFiwo62YJIYQQivR08PAoer+PD8yZA48/fuPXyO0xA0g5AOEjbvxcQlQzERHKPGZJSZCWljfptBAVqVr2mAkhhBCVmrv79We2nTBBed5s1aobu4b3VYGZEMIuIiJvOTq6wpohhAMJzIQQQoiKoNGAzQZLl1673tChSoA2bZpSv7jc64HmykPvEpgJ4aBu3bxlec5MVBYSmAkhhBAVacQIJeA6dQq6dSu63muvQUAAvPkmxMRc/7wqNXhfec4s4wwYk0unvUJUA/l7zOQ5M1FZSGAmhBBCVAb168P27UqQlpoKDz5YsE5iIrzzjvKp8q674Ndf4UqW4UL5dc5bjt9R6k0WoqqSwExURhKYCSGEEJWNpyf8+KMSpMXFwTPPQJMmefstFli+HIYPV9LLvfQSHDpU8DyBPfKW47eVdauFqDIkMBOVkQRmQgghRGUWEACzZsHRo0p5/XUICsrbHx8Pn3wCLVtChw7wxRdKqjmAAAnMhChM7dqgvZKbXJ4xE5WFBGZCCCFEVdGkCUydqjxj9vvvcM89yrxnuf79F55+GoKD4e67YcNu0NdT9iXuAktOxbRbiEpGq1UmmgbpMROVhwRmQgghRFWj0cCAAbBoEVy8CJ9/Du3b5+03GpVsj3feCY9egB+B40ZI2F1RLRai0skdzpiSUjpzugtxsyQwE0IIIaoyPz+ll2zPHti/H158EQID8/anGmAD8BbQ+S4leciZMxXUWCEqD3nOTFQ2EpgJIYQQ1UWrVjBjhjLUcfVqGDUKnPV5+6MTlXT79etD9+4wZ07e82hC1DD55zKTwExUBhKYCSGEENWNTgeDB8OCBXDpMjzlDc0AVb46O3bAE08oiUTuugsiI8FgqKAGC1H+pMdMVDYSmAkhhBDVmZcXPP0mvAZ8BoxrCM2b5+03mZTU+yNGKElDxo2DjRuVlPxCVGMSmInKRgIzIYQQorpr+AS41QE/oNdJ+PUZ+HeP8jxa/tT7ycnw7bfQt6+ST/yZZ5SeNau1wpouRFnJH5hJynxRGUhgJoQQQlR3Gj20mpq3vnsCXH4MXn8Qzp9XUu+PHg1ubnl1Ll+G2bOVZ9Hq1oVJk2DvXmXSayGqgZAQZdQvSI+ZqBwkMBNCCCFqgjr3Q/i9eevJ/8G6TnDmf9C/H/z0E8TFKSn477oL9PmShpw7B9OnKyn5GzeGN96AI0fK/S0IUZo0GggPV5ajouQ7B1HxJDATQgghagK1BrovgN4bwLuVss1qgD1PwYYekHIIXF2VSasjI5Ues7lzYdAgZTbeXCdPKpNcN28OrVvD++/DqVMV856EuEm5wxnT0mQuM1HxJDATQgghagqVCoL6woCd0OjpvO0Jf8P6bpC4J2+blxeMHQtr1kBsLHz9Ndx2m3KOXAcOwKuvQsOG0KYNvPsuHD1abm9HiJslz5mJykQCMyGEEKKm0ThDh8+h90bwbKxsM6fDnwMhtZAhiv7+MH48bNqkzJE2cyZ06eJYZ/9+mDIFmjVTetPeeEMJ3GR8mKjE8s9lJvOui4omgZkQQghRUwX1hoH7IPBWZd2QCFvuAGNq0ceEhMBzz8HffyufZD/8EDp1cqxz5Igy3LF1a+WZtMmT4d9/JUgTlU7jxnnLhw9XXDuEAAnMhBBCiJpN6wI9V4FPW2U94wzserx4QVRutsadOyE6Gj79VMnimH+448mT8MEH0KED1KuHbvJk3A4ckBT8olJo0SJv+dChimuHECCBmRBCCCF0nnDLMtB5KevnFsHZ+SU7R3g4PP88bNumDHecPVt5Jk2d76NGVBS6WbNo8sgjODdqBE8+CevWgdFYam9FiJJo0CAvAenBgxXbFiEkMBNCCCEEuNeFzt/krR96G6yWGztXSAg89ZTyTFpu4pD+/ZX85FeoY2Phq69g4EDlGbZ774UFCyQ1nihXWq3yWCQonbvZ2RXbHlGzSWAmhBBCCEX43RDUT1nOOAMXVtz8OQMDlcQh69ZBXByGOXNI6dEDW/550tLTYfFiuP9+CAiAfv2UHrfz52/++kJcR8uWyqvVKklFRcWSwEwIIYQQeZq8lLd89OPSPbevL5YxYzg9cybZ587B0qXwwAPg45NXx2yGP/6AZ55Rhke2bw/vvKNkfZTkIaIMyHNmorKQwEwIIYQQeYL7g9eVT6oJf0P832VzHXd3GDEC5s9XJrPetEnJ9ph/YimAvXvhzTeVedLq1VPqrF8PBkPZtEvUOLk9ZiDPmYmKJYGZEEIIIfKoVNDkxbz1s3PL/po6nZIoZOZMJQX/f//B229Du3aO9aKiYNYsGDAA/Pxg2DD45hu4cKHs2yiqrdat85b37Cm6nhBlTQIzIYQQQjiqcw9oXJTl88vAai6/a6tUyiflN95Q5j6LjobPP4e+fZVMDbkyM2HFCuX5tdBQaNsWXn9dmV/NcoNJS0SNFBysjJoF2LVLGU0rREWQwEwIIYQQjrRuEDJEWTYkQNyfFdeW8HB4+mnYsAESEpQkIQ8+qCQJye+//+C996BbNwgKgjFjYOFCSE6ukGaLqqVLF+U1K0uGM4qKI4GZEEIIIQqqc0/e8rklFdeO/Ly84O674ccf4dIlZWLrN95QEoTkl5AAP/0E992nBHC33goffqgEb5JARBSia9e85X/+qbh2iJpNAjMhhBBCFBQyuOKGMxaHWg2dOinPou3ZAxcvwnffwfDhSmKRXBYLbN0Kr7yiDHcMDob/b+++w6Oq8j+OvyfJpNFCCaG3BEIgoQYpP+mKqCBgQ1ZcFGyrspZ1RSxYUNeCDVdFlHVdXCuCKIIFBFEX6Z1AEkKUFkIxgJCe+f1xSCYNUpjkzkw+r+e5T84tc+c7ehzzyb33nD//Gf77X0hNta5+cSuFg9mqKhrvRqQsCmYiIiJSkl8taD7CtDOPwqHl1tZTlqZNYeJE+OwzOHrUDLl/773QoUPR4w4dMiNBjh8PYWHmatvUqbBiBWRlWVK6WK97d8ifWk/BTKyiYCYiIiKla+WGtzOWh78/DB0KL70Eu3ZBQoIZQGTECKhVq+ixGzbAs8+aUSEbNoQrroDXX4fERGtqF0v4+zvviE1MhMOHra1HaiYFMxERESlds8vAN9i0982HvGxr66msiAgzgMiXX5qrad9/D1OmmLnRCvvjD3PMXXdB+/YQHg533AELFmgQkRogfwAQ0HNmYg0FMxERESmdX7Bn3c5YHgEB5urYs8/Cxo1w8CD85z9w/fUlR3pMSoI33zTPrTVqBL16mWfVvvsO0tOtqV+qjAYAEaspmImIiMjZeertjOWVP7T++++bkR7Xr4d//AMGDTITX+fLyzODjDz3HAwbBvXrw5AhZoj+X37R5FdeQAOAiNUUzEREROTsml3qvJ1x73zIzbC2nqrk4wM9epirYsuXm9sev/gC7r4boqOLHpuZaY555BHzG33+82mvvgrbtmlYfg/UvDm0bGnammharKBgJiIiImfnFwwtRpl21jFIes/aeqpTnTowciS88oqZdTglBT74ACZNgjZtih574oR5Pu2eeyAmxowS+ac/mSH8ExMV1DxE/nNmp07B9u3W1iI1j4KZiIiInFvH+5ztuOfdb06z6hIWZiatfucd2LMHdu+G2bNh7NiSz6cdOgQffgg332wGEmnZ0jzHNns2xMcrqLmp2Fhne+tW6+qQmsnP6gJERETEzTWMhSYXQcpS+CMJfpsHba6zuirrtWtnlltuMc+gbdsGy5aZ5YcfzCiP+fbvN1fbPvjArDdpAgMHmmfZBg6Ejh3BZrPkY4hT4TtWt22zrg6pmRTMREREpGydpppgBrDtSWh1FfjYz/2amsTHB7p0Mcu990J2Nqxdayau/uEH+Plnc39cvpQU+PhjswA0bmwCWn5Y69RJQc0CCmZiJd3KKCIiImULGwyNzgxbdyIOds20th53Z7dDv37w0EPwzTdmHrRVq8ww/cOHQ+3aRY9PTYVPPzVzqEVHm6B21VVmMJH16zUSRTVp2dI8WggKZlL9yhXM3nnnnaquQ0RERNyZzQY9ZwJnruJsfRxO77eyIs9it5uRJaZMgSVLTFBbswaefx4uvxzq1i16/JEjMH++GUwkNhZCQuDii+GJJ2Dp0qK3SYrL2GzOq2a//mrGdBGpLuUKZjNmzGD8+PHs368vYBERkRqrYSy0v920c/6ADfed+3g5Oz8/M2H13/8OixbBsWNmnrQXXzTD7oeEFD3+1CkTyB5/3AS0kBAT2O65B+bNMxNli0sUvp1xxw7r6pCap1zBbNiwYaxbt44rrriCefPmVXVNIiIi4q66PAUBjUz7t0+cz53J+fH1hZ494b77YOFCc8Vs40b45z/huuvMJFuF5eaaWxxffRWuuQaaNYPwcJgwAd5+G+LizIAkUmGdOzvbup1RqlO5gtnMmTN5+eWXCQgI4NFHH+Uvf/kLR48ereraRERExN0ENIBuzzvX190FuZnW1eOtfH2hWze4804z7P7evZCcDO+/D3/5i5krrfjgIElJ8J//wK23msFDQkPN1bd//MNMhq3bH8tFV8zEKuUe/OPSSy9l8eLFXHbZZSxfvpyRI0eydKn+SiYiIlLjtJsAjfqZ9oldkPCGtfXUBDYbtG5t5kJ74w3YsgWOHoWvvoKpU2HAAAgIKPqaY8fMpNcPPQRDhkC9eibs/eUv8N57sGuX5lMrRYcOznZionV1SM1ToeHyQ0JCePHFFxk5ciRPPPEEkydPZtSoUVx11VWlHt+rVy+XFCkiIiJuxOYDvV6HJT0AB+x4FiJuBb9aVldWs9SvD5ddZhaAzEzYsAF++sksP/9swlu+vDzYvNkss2aZbQ0aQO/e0LevGZzkggtMgKvBmjeHwEDIyICEBNedNyvLzKrgp8mq5Cwq1TUGDRpE7969mTBhAgsXLmThwoWlHhcXF3dexZ3Lww8/TG5uLs8++2yR7atWreKFF14gMTGRJk2acMcddzB69Ohznuu///0v//rXvzh8+DBRUVE88sgjxMTEVFntIiIiHq9+N2h1Lfz2MWSkQsKbEHW/1VXVbAEBJmD17WsGFXE4ID4efvnFDNX/yy+wdWvRZ8+OHTOjRC5ZYtZtNnMbZJ8+zrAWFWUSRQ3h42Me19u+3dwdmptr7iytrE2bzGCc338PrVqZi5wdO7qsXPEilQpmCQkJPPnkk2zdupX69eszcOBAV9d1Vrm5ucyYMYN58+YxZsyYIvt2797NbbfdxqRJk5gxYwbLly/noYceIiwsjL59+5Z6vgULFvDCCy8wffp0oqKimD17NjfffDNLliyhQYMG1fGRREREPFPMNDMACA7Y8RxE3A722mW+TKqJzQaRkWaZMMFsO3nSjP5YOKwdPux8jcNhEsn27TBnjtlWt64ZQbLw0qKFV0+AHRFh/hFkZZnH+9q0qdx5HA5z92n+s2pJSWYO8WXLij7LJgIVDGbZ2dm8/vrrvPPOO+Tk5DB8+HCmTZtWbQFm9+7dTJ06lb1799KsWbMS+9977z06duzI3XffDUC7du3YsWMH77zzzlmD2axZsxg/fjwjR44E4JlnnuGiiy5i3rx53HrrrVX3YURERDxdvU7Q+jr49UPIPAJ73oMOd1pdlZxLnToweLBZwCSHpCQT0PLD2ubNRSe0PnHCJIlly5zbmjQpGdYaNqzez1KF2rd3thMTKx/MVq4sOYBIaqr5xz9vnglpIvnKfV1648aNjB49mrfeeou6devy6quv8sorr1TrVaU1a9YQFRXFokWLaNGiRYn969ato0+fPkW29enTh/Xr1+Mo5eHWo0ePkpycXOQ1fn5+xMbGsnbtWtd/ABEREW/T6UFnO/6fGkzC09hs5r6966+H114zV9OOH4cffzSTX195JTRtWvJ1KSlmYJFp0+DSS6FRI2jXDsaOhRkzTCLx4FEgCwez83nOLP9RPjAzH+QPv3DkCAwaBJdcYoKaCJTzitnTTz/NBx98QG5uLpdeeinTpk2jfv36VV1bCePGjTvn/pSUFJo0aVJkW+PGjUlPT+f3338vESJTUlIAaFrsC6dx48bs3LnTBRWLiIh4ufpdoPEASF0JJ3aaec2aXmx1VXI+goPhwgvNkm//fli7tuiSllb0dXv2mOWTT8y6j495Pq3wVbUuXUqOHumGIiKc7cqOzHj8OHz2mWk3agQ33wzjx8Pll5txWQC+/dbME/7jj2bQTanZyhXM5s6dS8OGDXnssccYNmxYlRSyb98+hg4detb9P/30E6Ghoec8R0ZGBv7+/kW25a9nZWWVOD49Pb3IMfkCAgLIzKz8nCwOh4PTp09X+vWulP8Z83+KlEV9RipC/UUAfFvfSkDqSgByd7xMZr3/O+ux6jMeqn59GDbMLAAOB7akJHzWrcNn/Xp8NmzAZ9MmbIX/veblOZ9X+/e/zcvsdhydOpHXrZtZuncnLzoagoLO+tZW9JkWLWyAqWnnzhxOny75e2RZVqzwITs7EIArr8wmNzcbu90M/vHxx7489pidlBQf9u6Fv/41hw8/rPh7SOnc6XvG4XBgK+fzmOUKZpdffjmPPvooISEh51PXOYWFhbF48eKz7i/PLZMBAQElAlj+elAp/8EHBgYWOSZfZmZmqceXV3Z2dpWOSFkZycnJVpcgHkZ9RipC/aWGc0QQ4xeGf84hfA59ze4t35NlL+X2t0LUZ7xEly5muekmyMkhaM8egrdvp9b27dTasYOgxERsubkFh9uys7Ft3ozP5s1mLjXA4etLRps2nO7Y0bl06EBeraLTL1Rnn8nLg4CA7mRm+rB9e06lfq9bvLgpYMZEaNVqL3Fxvxfs69kT/v1vP66/vhNHj9r54gs/5s7dTWys597+6Y7c5Xum+EWgsylXMHvxxRfPq5jysNvthIeHn9c5mjZtSmqxG3VTU1MJDg6mTp06JY7PH0AkNTW1yHunpqaWuCWyIux2OxGFr4FbKD09neTkZNq0aXNeYVNqDvUZqQj1FyngexvEPYkNBx3tq8iOerjUw9RnvFxMDFxxRcFqeno6Plu3mqtq69bhs3kztl27sBUast+Wm0vQ7t0E7d5Nw6++AsBhs+Fo3568rl3J7NyZ/WFhNLr4YgJLe96tinToYGYX2LcvgPDwKMr5u3WBPXuct2yOGdOUNm1K/m45fXoed9xh2h98EMENN1T+ji1xcqfvmcQK3AvrVVPcxcbGsmbNmiLbVq1aRY8ePfApZf6NBg0a0LZtW1avXl0wamNOTg7r1q3jT3/6U6XrsNlsBAcHV/r1VSEoKMjtahL3pj4jFaH+IkTeAjufAkce9r1zsXd/EnzOPvmT+kwNERxsRrkYNMi57dQp2LLFTIadv2zbVmQkSJvDgS0+Hp/4ePw+/ZQO+TvatoUePcwlp27doGtXMzhJFQzdHx1tglluro39+4Pp3Ln8r83LM4/hATRuDFFRQaWWeOut8PLLZoCRlSt9OXIkmFatXFO/uMf3THlvYwQvC2Y33HADY8aMYcaMGYwZM4YffviBb775hnfeeafgmLQzD6rm35Y5ceJEnn76aVq3bk1MTAyzZ88mIyODq6++2oJPICIi4qGCW0DTS+HAV3B6L6R8B82GW12VuKNatZwTYefLzDThbP16Z1jbssVsLyx/gJH8UTUAQkNNQCu8REWB3X5eZUZFOdtxcVQomMXHO8dG6dv37LnR1xduuMEMbgnw3//C1KmVKle8gFcFs/bt2/PGG2/wwgsv8N5779GiRQteeOGFInOYTZ48GTADmgBce+21nDx5kldeeYW0tDSio6N59913Nbm0iIhIRYXfbIIZwO53FMyk/AICzFWwnj2d27KzIS6OzFWrOL58OQ1/+w3fzZuh+ABrhw/D0qVmyWe3myRVPLBVYK61wsGs+FxkZVm92tkuNpNTCePHO4PZ3Lnw4INePXe3nIPHBrP8YFXcgAEDGDBgQIVeN2nSJCZNmuSy2kRERGqk5pdDYBhkHIJ9CyEjFQIbW12VeCq7Hbp0ITcigr2xsdSOiiI4IMBcjtqwwUyEvWmT+Vl8MrDsbLNv06ai21u0KBnWIiLMpatiOnVytis69kfhINet27mPbdsW+vc3Q+bHxZmPFxlZsfcT7+CxwUxERETcjI8d2t0IO54DRw7smQtRf7O6KvEmvr7mUlZUlJkUO19KStGgtnkz7NoFhUaEBGDfPrOcGWQEMM/BxcSY0SWjo007OpqIiFB8fc0pKnrFrHCQK3zl7WyuuMIEM4AlSxTMaioFMxEREXGddpNMMANzO2PH+3RfllS9Jk3Mcsklzm3p6WYOtfyglr8cP170tadPm3sPC99/CPiHhbEyIJrVp2PYtSOa3FUx+MZ0gtq1yyxn507zMzgYWrYsu/zhw+HvfzftJUvgnnvKfo14HwUzERERcZ267aHxAEhdCSd2wpH/QejZJ5wWqTJBQRAba5Z8Dgf89lvRK2ubN8Pu3SVff+gQ/ThEP5ZBDtDvzPZ27YpcWSMmxoytf2awkcxM5+kiI6GUgcFL6NzZ3GW5bx/88IPJihq0tOZRMBMRERHXCr/ZBDOAhLcUzMR92GzQurVZRo1ybj950lxd27bNjJGf//Pw4ZLnSEoyyxdfOLfZ7SaFxcSQFhrNZXkxbCOaTh1bA2UnM5sNLr0U3n7bBLsVK+Cyy87704qHUTATERER12p5Fay/G7J+h98+gm7PQnAzq6sSObs6dczwicWGUFz9ZSoPXbGVaLZxZfutDGywzYS2U6eKvj4722zfto0w4Mszm7M+qwWxHc1IIoWXtm1LDDgybJgJZgArVyqY1UQKZiIiIuJafsEQcTvs+AfkZUP8a9DtH1ZXJVJhXS9uzE/+Q/k+ayiLHZDwC2b26F9/dV5Zy7+6tnNnkUmyAfyzTpm52davL3rigABzha1QWBsU1gk/IsjBzsqV1fcZxX0omImIiIjrRU6GnTNMMEuYBZ0fBnvZgyaIuJPAQPOI2v/+B4mJcOgQhIX5mCtebdua4RTzZWVBfDyv3baNE/8zV9mGt9pBwL4kE+YKy8w0E2hv2VKwqRFwGj8SaM/OXzqRPbUT9q5ngluHDqYY8WoKZiIiIuJ6QU2hzfWQ9G/IToOEN6HT362uSqTCLrzQBDOAn3+GK688y4H+/hAdzb8zotnAdfj4wOl4IC/dTE4WF2fG3c9fEhJKXGGzk0Mn4ujkiINnP3Pu8PExg47kX2GLijJX3CIjISSkKj62WEDBTERERKpGx79B0nuAA7ZNh7bjgXpWVyVSIb17O9ubN58jmGEujOUPld+unbljEYKck1kXlpVlLsMVCmzHft5B8N5dBJJZ8sSJiWYpPOgIQFiYCWgdOzrDWmQktGkDfvpV35Po35aIiIhUjZBoiLgFEmdDzknY9CB0fdPqqkQqpFMnZ7vwxNGl2bfPDHUP5ZhY2t/feQXsqqsAOJEMjdvm0JY9XN89jsevLXSFLS7OefLCDh0yS/EH0/z9ISKi9NBWv34ZxYkVFMxERESk6nR5Gn771IzQuOc/+IZeCrS3uiqRcgsPNxeecnLKDmaF93fsWPH3at0aGoT6kXi4Pa/92p7HplzhnJ89Lw/27jXD+u/cCbt2mWXnThPMisvKcoa64ho3doa0wqGtbVtdZbOQ/smLiIhI1QlsBF2fhrV3AOC/7iZqtXgTKOtygoh7sNuhfXsTuuLjTUA7W3bJv40RynHFrBQ2G/TqBYsXw7FjkJxsshJgnjPLn4Ot+Fj6aWmmuOKBLSHBBLTiUlPN8uOPJT9sRIT5wMWX5s3LN1u2VJqCmYiIiFStiNvh8CpInostL4P2++4ip7ENOkywujKRcomKMsEsK8uEpYiI0o8rHMwqc8UMzCiQixeb9tq1hYLZuYSEwAUXmKWw3FxTcOGwlt9OSSl5nuxs80FLuzQYFGQuH5YW2po2xXlpTypLwUxERESqls0Gvd+B9P1w6Ht8807hu+5GODjfDKPf6IIyTyFipcJXv+Lizh7MzvdWRjBXzPKtXQvXXlu58wBmEuvwcLMUv8p2/HjpgS0hwQznX1x6unPetuJq1TL/UEq72hYWptBWTgpmIiIiUvV8/WHAQnJWTcJv3ydm2/4vzBI2FDo/BGGD9QucuKXiwWzkyNKPyw9mYWGVH1+jeDCrMvXqnf0q2759JqAVX5KSzFW14k6dMkNWbt5ccl+dOqUHtnbtFNqKUTATERGR6mGvTVbPf7E3pzOt02bhk3HQbD+0zCyN+kLME9D0YmvrFCmmeDArzf795rEtgM6dK/9eYWHQsqUZ52P9epOTfH0rf74K8/V1Pst20UVF9+XkwG+/lR7a9uwxxRZ38iRs3GiW4mrVMgEt/6pe4Xbr1uaZtxpEwUxERESqj83GsXqXE9ZrMsGH5sGO5+CP3WbfkVWwfBi0GAM9X4FarSwtVSRfZKSzvWtX6cf88ouzXXjus8qIjTXB7I8/zJgelRlIpEr4+Znw1K4dXHJJ0X3Z2eZ5tuKBLTHRbM/LK3m+U6dg61azFOfjA61aOYNa8eBWt25VfEJLKZiJiIhI9fMNMHOctbvJDKe//Wk4vt3s27cADn0Pff8DLa6wtk4RzIWdZs3gwAGTNUqzapWz3bfv+b1fr16wYIFpr13rRsHsXPKHr2xfynQYWVnmilrhsLZ7t7k1Mjm59Nsj8/LMvuRkWLas5P5GjYoGtcLhrZ5nTmSvYCYiIiLW8fGDNuOg9VhI/gA2/g0yUiH7OKwcBdGPQcxjeg5FLNe+vQlmR46Y0elDQoruL3zFrE+f83uv4s+Z/fnP53c+y/n7O+dKKy4311weTEoyYS1/yV8/frz0cx45YpY1a0rsCgoMpH2XLtjmzoUOHVz8YaqOgpmIiIhYz+YDbcdD8xGw+hbYO89s3/aEmZy658vmGBGLtG8PP/xg2omJ5nbDfFlZsG6daYeHQ2jo+b1X4XNX6QAg7sDXF9q0McuQIUX3ORxmQrezhbZ9+0o9pS0jg7pr1pC1cCH8/e9V/hFcRcFMRERE3Id/CFz4Cex8ETae+YUqfiY4ciH2NV05E8sUvkMvIaFoeFq/3jnC/Pnexgjmalz79uZ9Nm0ywc/f//zP63FsNmjY0CyFLyPmy8gwtzoWC2x5iYmcCgrCb/To6q74vCiYiYiIiHux2SDqfghoBKsngSMPEl6H4ObQearV1UkNVTyYFfbOO872oEGueb/YWOeUYtu3Q/furjmvVwkMNBPGFZs0LuP0aeLj4ohq3tyiwipH9wSIiIiIe2p3I/R+17m++SFI/tCycqRmKzypdOFgdvQofPCBaderB2PHuub9evZ0tjdtcs05xb0pmImIiIj7avdn6Pasc331JPi9lElsRapYeLiznR/Mdu82A3NkZJj1iROhdm3XvF/Xrs52afM2i/dRMBMRERH3FvUAhE8y7dx0WDkGstIsLUlqnuBgaNHCtDdtguHDzVW0xYvNtoAAuPNO171f4WCmK2Y1g4KZiIiIuDebDWL/CQ3OjLZwag+sdeFvwCLl1KOH+ZmZCd9849zeqBF8/nnRq2rnKzTUzJ0G5oqZw+G6c4t7UjATERER9+cbCP0/A3uIWf/1A0j+yNKSpOZ56inwKzR0nt0O06bB1q3mCpqrdetmfqalmam+xLspmImIiIhnqNUKer3hXF/7Fzi937p6pMaJiYGZM03b1xfmz4cnnoAmTarm/XQ7Y82iYCYiIiKeo804aH2daWenwS83meH0RarJX/4Cq1aZ2wtHjKja99IAIDWLgpmIiIh4ltjXIejM/EQp30H8P62tR2qcPn2gc+eqf5/8WxlBwawmUDATERERzxLQAPoUmt9s04PwR7Jl5YhUlYgICAoybd3K6P0UzERERMTzNL0Y2p8ZmTE3HdbdpWHrxOv4+prn2sDMmXbypLX1SNVSMBMRERHP1O0ZCDoznviBr2DvfGvrEakChW9n3LrVsjKkGiiYiYiIiGey14WeM53r6/8K2Sesq0ekCmhkxppDwUxEREQ8V8srodnlpp1+ADY/Ym09Ii6mkRlrDgUzERER8Vw2G8T+E3zPjJAQ/084us7amkRcqEsXZ1vBzLspmImIiIhnq90GYh4/s+KAdZM1EIh4jTp1IDzctLdsgdxca+uRqqNgJiIiIp6v471QN8q0j/4CyR9YW4+IC+XfzpieDomJ1tYiVUfBTERERDyfjx16vOxc3/QAZP9hXT0iLqSJpmsGBTMRERHxDs0ugeYjTTv9AOx4ztp6RFxEIzPWDApmIiIi4j26v2iungHEvQB/JFtajogr6IpZzaBgJiIiIt6jbnuIvNu08zJh49+trUfEBVq2hJAQ01Yw814KZiIiIuJdOj8CgY1Ne+88SF1pbT0i58lmc97OuH8/HDlibT1SNRTMRERExLv414MuTzvXN9wPjjzr6hFxAU007f0UzERERMT7tLsJ6kWb9rG18OtH1tYjcp70nJn3UzATERER7+PjC91nONc3TYWcdOvqETlPumLm/RTMRERExDs1uwSaXmLap3+DXa9aW4/IeejUCXx9TVvBzDt5bDB7+OGHefDBB0ts/+yzzxg5ciTdunVj2LBhzJ49m9zc3LOeJzs7m+joaCIjI4ssL7/88llfIyIiIh6i+wywnfl1Z/szkJFqbT0ilRQYCB07mvaOHZCVZW094np+VhdQUbm5ucyYMYN58+YxZsyYIvu+/PJLHnvsMR577DF69+7N9u3bmTZtGllZWdx1112lni8pKYns7GwWLlxIw4YNC7YHBwdX6ecQERGRahASDe0mwe63IeckbH0cer1hdVUildK1K2zfDtnZEBdX9PZG8XwedcVs9+7djBs3js8//5xmzZqV2P/BBx8wZswYrrnmGlq1asWll17KTTfdxLx58856zvj4eOrUqUPHjh0JDQ0tWGrVqlWVH0VERESqS5cnwa+2aSfOhuNx1tYjUkl6zsy7eVQwW7NmDVFRUSxatIgWLVqU2H///fczceLEEtuPHz9+1nPu2rWLiIgIl9YpIiIibiSoCXSaYtqOXNj4gLX1iFSSRmb0bh51K+O4cePOub9nz55F1k+cOMGHH37IhRdeeNbXxMfHk5OTw6RJk4iLi6NJkyZMmDCBUaNGVbpOh8PB6dOnK/16V0pPTy/yU6Qs6jNSEeovUlGW9ZlWtxMY/yY+GQfgwCIyfl1MXuig6q1BKkXfM07t2wOYx202bMjl9OlMS+txV+7UZxwOBzabrVzHuk0w27dvH0OHDj3r/p9++onQ0NByn+/UqVPccccdZGZm8sADZ//LWEJCAn5+fvz1r38lNDSUFStWMHXqVLKzs7n66qsr9BnyZWdnExfnXrdJJCcnW12CeBj1GakI9RepKCv6TIOQW2mb8jgAeevuJa71XLD5VnsdUjn6njEaNuzC0aN2Nm3KY8eOOMr5O3+N5C59xt/fv1zHuU0wCwsLY/HixWfd36BBg3Kf6/Dhw9x2223s3buXOXPm0LJly7Me+/XXX5OXl0dQUBAAUVFRHDx4kDlz5lQ6mNntdre5PTI9PZ3k5GTatGlT8BlFzkV9RipC/UUqytI+44gkb8UCfI5vJjgznuhaG8ltfUP11iAVpu+Zorp182HZMkhLsxMS0olmzRxWl+R23KnPJCYmlvtYtwlmdrud8PDw8z7P7t27ufnmm8nJyeH9998nMjLynMcHBASU2BYZGcmXX35Z6RpsNpvbjeoYFBTkdjWJe1OfkYpQf5GKsqzPxL4My4YAELDzSWg/Hvw04Jcn0PeM0aMHLFtm2vHxQbjJtQC35A59pry3MYKHDf5Rlr179zJhwgSCg4P55JNPygxlaWlpxMbGsnDhwiLbt27dSntzE6+IiIh4k7DB0HykaacfgLiXrK1HpII0MqP3cpsrZq7w0EMPkZWVxYsvvoifnx+HDx8u2Jf/fFpaWhoAISEhhISE0K9fP1566SUaNGhAy5Yt+fbbb/niiy946623rPgIIiIiUtW6PQ8HFpsRGuOeg4ibIaip1VWJlItGZvReXhPMDh06xJo1awBKHVFx165dAEyePBmAuXPnAvDss8/y2muv8eijj3L06FHCw8OZOXMm/fv3r6bKRUREpFrV6wgRt0PC65BzCrZMg95vW12VSLlERkJAAGRmKph5G48NZvnBKl9YWFhB+KrI64KDg5kyZQpTpkxxaX0iIiLixmIeg+S5kH0Ckv4FkX+FkBirqxIpk58fdO4MGzZAfDycPg169M47eNUzZiIiIiLlEhgKnR8ybUcebLjf2npEKiD/ObO8PNi2zdpaxHUUzERERKRmirwbarU27ZRv4cDX1tYjUk4aAMQ7KZiJiIhIzeQbCF3/4VzfeD/k5VhXj0g5aQAQ76RgJiIiIjVX6+ug4QWmfXw7JL1rbT0i5dCli7OtYOY9FMxERESk5rLZoEehucy2PArZJ62rR6Qc6teHVq1Me8sWcDisrUdcQ8FMREREarbQ/4OWV5l2xiHY8Zy19YiUQ/5zZidOQHKypaWIiyiYiYiIiHR7Fnzsph03A04kWFuPSBkKDwCyaZNlZYgLKZiJiIiI1ImAyHtNOy8T1k/W/WHi1jQyo/dRMBMREREBiH4Uglua9sFvYO9n1tYjcg4amdH7KJiJiIiIANhrQ89Xnevr79FAIOK22rWD2rVNW8HMOyiYiYiIiORrMRqaXWba6fth6xOWliNyNj4+EBNj2nv2mEFAxLMpmImIiIjks9kg9jUz+TTArlfg9y2WliRyNnrOzLsomImIiIgUVrsddHrItB25sPpmyMu1tiaRUhR+zmzjRsvKEBdRMBMREREprtMDUDfKtI+thV2vnvt4EQv06OFsb9hgXR3iGgpmIiIiIsX5BkDvdwCbWd/yCPyRZGlJIsXFxICfn2krmHk+BTMRERGR0oT2gw53mnZuOqy+VXObiVsJDITOnU17xw5IT7e2Hjk/CmYiIiIiZ9P1GQhuZdqHlkHSu9bWI1JM/u2MubmwxYvGqcnLg4QEWLYMVqyA+HizzZspmImIiIicjb0OXPCWc33DfXB6n3X1iBTTs6ez7Q23M6anw5NPQuvW0KEDXHQRDB4MkZHQoAFMmACrV1tdZdVQMBMRERE5l2bDoc0Npp19HH6ZpFsaxW140wAgcXHQvTs89hjsK+XvH8ePw3/+A336wNVXw+HD1V9jVVIwExERESlL7KsQ1My0U76FxLfOfbxINenSxUw2DbB+vbW1nI+EBBgyBHbtMuu+vjB8ODzwgFlGj4a6dZ3Hf/aZ+ezedPVMwUxERESkLP71ofcc5/rG++HkbuvqETmjVi3o2NG0t22DzExr66mM9HQYORJSUsx6166wdSssWQLPPWeWBQvg0CGYNQtCQ81xKSkmzC1dal3trqRgJiIiIlIezYZDxG2mnXMKfpmgiafFLeQ/Z5adDdu3W1tLZTzyiPNKWXQ0fP89REWVPC4wEG67zQxyMnCg2Xb6NIwa5R1XzhTMRERERMqr+wyo1da0D/8MO2dYW48Inv2c2dat8PLLph0QAJ9+agb5OJcmTeDrr00gAxPORoyA336r2lqrmoKZiIiISHnZa0Pf9yiYeHrzI3DEC/5ULx6tcDDztOfMnnjCOZbO4487b8ssS2AgfPQRDBpk1o8cgWuu8cxbOfMpmImIiIhUROP+0HmqaTty4OdxkHXc2pqkRuve3dn2pGC2ZYsZxAPMVbC7767Y6wMDYf58aHvmIvaaNTBtmmtrrE4KZiIiIiIVFfM4NOxj2qf2wNrbNYS+WKZOHTPnF5iwk5VlbT3llX8LI8CDD0JQUMXPUb++CXf+/mb9hRdg1SrPjDieWbWIiIiIlXzs8H8fgr2eWf/1I0j6t6UlSc12wQXmZ2YmbN5sbS3lcfSouRURICQEbrml8ufq3h2mTzdthwPuuMOfrCzbeddY3RTMRERERCqjdhvo/bZzfd1dkLbVsnKkZuvd29n2hBEK//1vyMgw7RtvhODg8zvf3/5mJp4GiI/3Ye7csPM7oQUUzEREREQqq9U1EH6zaeeehpWjIfOYpSVJzeRJwczhgLcL/U3j9tvP/5y+vmaOM19fsz5vXuj5n7SaKZiJiIiInI+eM6H+mWHx/kiCn6+DvBxra5Iap2tXM9w8uH8wW7vWOW/ZgAEQGema83btCk89ZdodO552zUmrkYKZiIiIyPnwC4IBCyDgzF/oU76DzVOtrUlqHH9/5+iMCQlwzI0v3P7nP872hAmuPfeDD8K+fad56aXdrj1xNVAwExERETlftVpB/3lg8zPrcTNgz/vW1iQ1TuHbGdeuta6Oc8nKcg76ERgIV1/t+veoXx9snjf2h4KZiIiIiEs0HgA9X3Gur54IB76xrBypeTzhObOlS82IjACjRkHdutbW404UzERERERcpf0dEHGraedlw49XwpFfrK1Jaoz8IfPBfYPZxx872+PGWVeHO1IwExEREXEVmw1iX4eWV5r13NOw4jJI22ZtXVIjtGsHjRqZ9urV7jfneWYmfP65adetC5dcYmk5bkfBTERERMSVfPyg338hbIhZz/odlg+DE/HW1iVez2ZzXjU7ehSSkqytp7hvvoETJ0x71CjzjJk4KZiJiIiIuJpvIAz4HBrEmvX0g/DdhXBsvaVlifdz5+fMPvnE2b72WuvqcFcKZiIiIiJVwV4HBi2BkC5mPfMwLB0EKd9bWpZ4tz59nO2ff7aujuLS02HhQtMOCYFhwywtxy0pmImIiIhUlcBGcNEPENrfrOf8ASsuhd8+tbYu8Vp9+4Kvr2n/8IO1tRS2ZAn88Ydpjxlj5l2TohTMRERERKqSfwgM/gaajzTreVnw07Ww8QHIy7G0NPE+depAjx6mvX07HDlibT35Pi30twjdxlg6BTMRERGRquYXBP3nQ7ubnNviXoDvh5rnz0RcaOBAZ3vlSuvqyJeZCV99ZdohITB0qKXluC0FMxEREZHq4OMHvedAz1fB5me2pa6EJd3h4LfW1iZepXAwc4fbGVesgJMnTXvkSLDbLS3HbSmYiYiIiFQXmw0i/woXrYSg5mZbxiFYfgmsuQ2yT1pbn3iFCy80XQ3cI5jlz10GMHq0VVW4PwUzERERkeoW2hcu3QhNC82wmzgbFsdAyjLr6hKvEBIC3bqZ9pYt8Pvv1tWSl+ccjTEwUJNKn4uCmYiIiIgVAkPNcPq9ZoFfLbPt1K/w/UWw+hbISrO0PPFs+bczOhzw44/W1bFmDRw88xjlxRdDrVrW1eLuFMxERERErGKzQfvb4LKt0HiQc/vud+CrTrD3c6sqEw/nLs+Z6TbG8lMwExEREbFa7bYwdBnEvg5+tc229IPw4xgztH76IWvrE4/Tv7+z7Q7BzMfHDPwhZ+exwezhhx/mwQcfLLH9hhtuIDIyssgybty4c57rv//9L0OHDqVLly6MHTuWrVu3VlXZIiIiIqWz+UCHO+DyHdDsMuf23z6Fr6Ig6T/mvjSRcmjYEGJiTHvjRjh+vPpr2LkTdu0y7QsvhNDQ6q/Bk3hcMMvNzeW5555j3rx5pe6Pj4/n8ccf56effipY3nzzzbOeb8GCBbzwwgvcc889zJ8/n9atW3PzzTdz7NixqvoIIiIiImdXqyUMXAT9/gsBDc22rN/hlwmw4lLzHJpIOQwZYn7m5cHSpdX//gsWONujRlX/+3sajwpmu3fvZty4cXz++ec0a9asxP5Dhw6RlpZGt27dCA0NLVhCQkLOes5Zs2Yxfvx4Ro4cSUREBM888wxBQUFnDX4iIiIiVc5mgzZ/gsvjoHWhO38OfgNfdYZd/9TVMynT8OHO9tdfV//7z5/vbF95ZfW/v6fxqGC2Zs0aoqKiWLRoES1atCixf9euXfj4+NCuXbtyne/o0aMkJyfTp0+fgm1+fn7Exsaydu1al9UtIiIiUimBofB/H8DAL53znuWcgvWT4YcRkHHY2vrErQ0caIaoB1iypHqz/G+/wbp1pt29O7RpU33v7an8rC6gIsp6Viw+Pp66desybdo0Vq1aRa1atbjkkku444478Pf3L3F8SkoKAE2bNi2yvXHjxuzcubPSdTocDk6fPl3p17tSenp6kZ8iZVGfkYpQf5GKUp+ppPpDYMha7NunYU9+x2w7sJi8xV3I6vkv8kIHnvv1Hkx95vz07x/Ad9/5sn8/rF2bTnR09aSzTz7xA8zv3yNGZHH6dE61vC+4V59xOBzY8mf7LoPbBLN9+/YxdOjQs+7/6aefCC3jicGEhAQyMzOJjY1l0qRJ7Nixg+eee44DBw7w/PPPlzg+/19W8dAWEBBAZmZmJT6FkZ2dTVxcXKVfXxWSk5OtLkE8jPqMVIT6i1SU+kwlBdxO3RZdaXPwMey5x/DJSCHg58s52PBmDja8xQwg4qXUZyqna9dQvvuuFQDvvXeMiRNTquV9P/qoA/nBrHPnBOLiMqrlfQtzlz5T2gWi0rhNMAsLC2Px4sVn3d+gQYMyz/HMM8/wyCOPUKdOHQA6dOiA3W7nvvvu44EHHqBRo0ZFjg88c203KyuryPbMzEyCgoIq+hEK2O12IiIiKv16V0pPTyc5OZk2bdqc12eSmkN9RipC/UUqSn3GFaLIzrgMn/U343t4OTYcNDv6No3tB8jq+Q7Y61pdoEupz5yfiRNtzJhh2r/80oQXXqhf5e95+DBs3Gj+XUVE5DFiRFvKedHIJdypzyQmJpb7WLcJZna7nfDw8PM6h6+vb0Eoy9ehQwfA3LZYPJjlDyCSmppa5L1TU1Np0qRJpeuw2WwEBwdX+vVVISgoyO1qEvemPiMVof4iFaU+c56C28FFS2HH87DlYXDk4ZfyFX4/DoYBn0PdSKsrdDn1mcqJioJu3WDTJtiwwZejR4Np2bJq33PpUjMSJMCVV/pQq5Y1/97coc+U9zZG8LDBP8oybtw4Hn300SLbtm7dit1up00pTxw2aNCAtm3bsnr16oJtOTk5rFu3jtjY2KouV0RERKTybD7Q+UEYtAT8z1wFObETvrkADn5rbW3iVgqPiJg/4XNVKjxMvkZjLD+vCmYjRoxg/vz5fPzxx+zdu5fFixfz/PPPM2nSJGrXrg1AWloaaWlpBa+ZOHEi7777LgsWLCAxMZGHHnqIjIwMrr76aos+hYiIiEgFNB0Gl6yFetFmPfsErLgcdv/L2rrEbYwZ42x//HHVvteRI/DNN6bdvDn06lW17+dN3OZWRle4/vrr8fHx4b333uOpp54iNDSUG2+8kVtvvbXgmMmTJwMwd+5cAK699lpOnjzJK6+8QlpaGtHR0bz77rvleqZNRERExC3UCYdhq2DVeNi3EBw5sHoS/LEHujxJtT7gI26nc2ezbN8OP/8MSUlQztmlKuyTTyDnzACM48aBj1ddBqpaHhvM8oNVcePGjTvnsPqlvW7SpElMmjTJZbWJiIiIVDt7bbjwM9hwH8TPNNu2PwWnfoU+c8DHbm19YhmbDcaPh6lTzfr778O0aVXzXu+/72yPH1817+GtlGFFREREvIWPL8S+Cj1eAc5cJUueCyuvhBzr53QS61x/vfPC6dy5VTPZ9K5dsGqVaUdHQ5curn8Pb6ZgJiIiIuJtOt4N/T8DnwCzfmARrBgOWcetrUss07IlDB5s2omJsGyZ699j1ixne8IE3UFbUQpmIiIiIt6o5RgY/DX4nZlKKHUlLBsMGanW1iWWuf12Z/v111177tOn4d//Nu2AALjpJteevyZQMBMRERHxVmGD4KLlEHBmLtffN8J3/eHUb5aWJdYYPRrOTOPLF1/Ar7+67tzvvw/5A59fdx00bOi6c9cUCmYiIiIi3qxBT7joRwhuYdZPxsN3/wfHd1pbl1Q7ux1uu8208/Lg+eddc96cHHjuOef6nXe65rw1jYKZiIiIiLer1xEu/hnqdDDrp/fB0v5wbL21dUm1u+MOqFXLtOfMgQMHzv+cH31khuAHuOgizV1WWQpmIiIiIjVBrVZw8Y9Qv7tZzzwCSwfDoeXW1iXVqlEjE84AMjPhqafO73yZmfDYY871Rx45v/PVZApmIiIiIjVFYGMYuhwaDzDrOSdh+XDYO9/auqRa/e1vULu2ab/1FmzdWvlzvfqq82rZ4MEwYMD511dTKZiJiIiI1CT+9WDQ19B8pFnPy4KfroHEt62tS6pNWBg89JBp5+XBXXeZnxW1Zw9Mn27aPj7wyisaIv98KJiJiIiI1DR+QdB/PrSdYNYdebDmVtj+j6qZeVjczr33Qtu2pr1yJcycWbHX5+XBjTfCH3+Y9dtu04TS50vBTERERKQm8vGDPv+Cjn9zbtv8EGy4zwQ18WqBgWbwj3wPPgirV5f/9Y88YgIdQOvW8Oyzrq2vJlIwExEREampbD7QYwZ0KzTW+a5XYNUEyMu2rCypHoMHwz33mHZmppnnbM+esl83Zw784x+m7eMD770HdetWVZU1h4KZiIiISE3X6QHoPccENYDk92HlaMg5ZWlZUvWee845YEdKCvTvD9u3l36sw2Fuebz5Zue2l1+GgQOrvs6aQMFMRERERCB8Ilz4GfgEmPUDi+G7AXB6v7V1SZXy94fPPoOoKLO+fz/ExprJp/OfHwPYtQuuugruvtu57Z57YPLkai3XqymYiYiIiIjRcjQM/hrsZ+5L+30DfHMBHNtgaVlStRo1gh9+gB49zHpGBkyZYrZ37w7t20PHjrBggfM1U6fCSy9pFEZXUjATEREREaewQXDxz1CrjVlPPwDf9Ye9C871KvFwoaHw009m6Px8mZmwaRMkJjq3NWoE8+fDM88olLmagpmIiIiIFBUSDZeshkb9zHruafjxStjxnIbT92JBQfDaa7BtG9xyC4SHm8E9AgOhXz9ze2NSEowZY3Wl3snP6gJERERExA0FNoahy2D1LWYwEIBND0LaNrjgLfALtrY+qTKdO8Ps2VZXUfPoipmIiIiIlM43EPr+B7pMd25Lfh++7QsnEqyrS8QLKZiJiIiIyNnZbBD9CFz4KfjVMtvStsA3sbBvobW1iXgRBTMRERERKVurq+GStVC3o1nPPmHmOlt/D+RmWFmZiFdQMBMRERGR8qkXBZesgVZjndt2vQrf9IbjO6yrS8QLKJiJiIiISPnZ68D/fQix/3RORp22Bb7uCfGvgyPP2vpEPJSCmYiIiIhUjM0GHe6E4WuhXrTZlpsB6+6C7y+GP/ZYW5+IB1IwExEREZHKCYkxtzZ2KDQr8aHvYXGMrp6JVJCCmYiIiIhUnl8QxL4Gg7+F4FZmW84pc/Xsuwvh902WlifiKRTMREREROT8Nb0YLt8GEbc7tx1ZZZ49W3c3ZB23rjYRD6BgJiIiIiKuYa8DF7wJQ5ZBnQ5mmyMP4mfCoo6w57/gcFhbo4ibUjATEREREddqMgQu2wJdnwHfILMtIwVWjYelA+HIGmvrE3FDCmYiIiIi4nq+AdB5KoyIgxajndsP/wjf9oafxsLJ3ZaVJ+JuFMxEREREpOrUag0DFsDAr6BOe+f23z4xtzeu+ytkHLauPhE3oWAmIiIiIlWv+WVw+XaIfR0CG5ttjhyIfw2+CIct0yDrd2trFLGQgpmIiIiIVA8fO3S4A0YmQvQ08A0223NOwrbpsLANbHkMstKsrFLEEgpmIiIiIlK97HWgyxNwRSJE3AY2P7M9+wRse9IEtK1PKKBJjaJgJiIiIiLWCGoKF8yCkQkQfnOhgHYctj4On7fCvv0R/HKOWFqmSHVQMBMRERERa9VuA73fhpHxED4JbL5me85J7AkvE5N0BfZNf9UojuLVFMxERERExD3Ubgu93zkT0G4BH38AfBxZ2JPnwKIO8PM4+H2TtXWKVAEFMxERERFxL7XbQe/ZMCqZ7Ih7yLWdGSTEkQe/fgRLusPy4XDwO3A4rK1VxEUUzERERETEPQU1JTv6abaGLyIr6jEICHXuO/gNLB8Gi7vA7n9BboZ1dYq4gIKZiIiIiLi1XN+65EQ+AKOSIfafUKuNc+fxbbB6EixsDVuf1GTV4rEUzERERETEM/gFQ4c7zSiOF34Kjfo592WkwtbH4POWsPoWOL7DujpFKkHBTEREREQ8i48ftLoahv0Mw1ZBq2vBdubX2rxM2P0OfNUZll+q59DEYyiYiYiIiIjnatQHLvwYrkiCjn8De13nvoNfn3kOLRriXzcTWIu4KQUzEREREfF8tVpDjxkwei/0eLnYc2g7YN1dsKA5rL0D0rZbVqbI2SiYiYiIiIj3sNeFjveceQ5tHoT2d+7L+QMS3jRX0JYOgl8/gbxsqyoVKULBTERERES8j48ftLoKLl4Jl26GiNvAr5Zzf+oP8PNYM5rjlsfh9H6rKhUBFMxERERExNvV7wIXzILR+6HnTKjb0bkv/SBsewIWtoIVl8Pe+ZCbZV2tUmP5WV1AZT388MPk5uby7LPPFmwbMmQI+/eX/teO999/n169epXYnp2dTffu3cnOLnoZ+/bbb+fee+91bdEiIiIiYh3/ehA5GTrcBYe+h4Q3YN9CcOSCIw8OLDZLQCi0/TOET4J6UVZXLTWExwWz3NxcZsyYwbx58xgzZkyRffPmzSM3N7dgPS8vj9tvv53atWvTvXv3Us+XlJREdnY2CxcupGHDhgXbg4ODq+YDiIiIiIi1bDZoMtQsp/dB4juQ9C6c/s3szzwMO180S8M+ED7RDM/vX9/ausWreVQw2717N1OnTmXv3r00a9asxP4GDRoUWX/rrbfYu3cvS5Yswc+v9I8aHx9PnTp16NixY6n7RURERMSLBbeALo9D9KNwaBnsngP7Poe8M7czHv3FLOvugmaXQ5vrofnl4BtoZdXihTzqGbM1a9YQFRXFokWLaNGixTmPPXToELNmzeLee+8lNDT0rMft2rWLiIgIV5cqIiIiIp7ExxeaDjNzoo05AD1egZAY5/68LNi3AH66GuY3gV8mQcr3kJd71lOKVIRHXTEbN25cuY+dPXs2DRs25LrrrjvncfHx8eTk5DBp0iTi4uJo0qQJEyZMYNSoUZWu0+FwcPr06Uq/3pXS09OL/BQpi/qMVIT6i1SU+oxUlDV9Jgha3QItb8YnbSO++z7Cb988bJmHzO7s45D0L0j6F3mBTcltNprcZqPJa9gXbL7VWKeUxp2+ZxwOBzabrVzHuk0w27dvH0OHDj3r/p9++umcV74KO378OPPnz2fKlCn4+p77P46EhAT8/Pz461//SmhoKCtWrGDq1KlkZ2dz9dVXV+gz5MvOziYuLq5Sr60qycnJVpcgHkZ9RipC/UUqSn1GKsq6PhMEfjdB6xuoc3odDU8sIeTkcnwd5o/wPhkH8Ul6E3vSm2T7NiCt9mB+rzOUk8E9wOY2v2rXSO7yPePv71+u49ymt4SFhbF48eKz7i/+/Ni5fPvttzgcDkaOHFnmsV9//TV5eXkEBQUBEBUVxcGDB5kzZ06lg5ndbneb2yPT09NJTk6mTZs2BZ9R5FzUZ6Qi1F+kotRnpKLcq8/EADeRmXMa35TF+O77GN/UpdjOPI9mzz1G6PHPCD3+GQ7/huQ2HUFuk8vJDR0MfhpYrrq4U59JTEws97FuE8zsdjvh4eEuOdeyZcsYNGgQtWrVKvPYgICAEtsiIyP58ssvK/3+NpvN7UZ1DAoKcruaxL2pz0hFqL9IRanPSEW5V58Jhrp/hg5/hqzjcOAr+G0eHFwCuRkA2LKO4vfre/j9+p4ZKCRsCDQfaQYQqdXS4vprBnfoM+W9jRE8bPCP8tq4cSN9+vQp87i0tDRiY2NZuHBhke1bt26lffv2VVWeiIiIiHgL/3rQ5k8wYD5ceRgu/ARaXQt+hS4Q5GaY+dHW/sVMZL2kO2x+FI6sNvOnieBGV8xc5cCBA6SlpdGhQ4dS96elpQEQEhJCSEgI/fr146WXXqJBgwa0bNmSb7/9li+++IK33nqrGqsWEREREY9nrw2trjFLTroZfn//IrOk73ce9/sms2x/CgIbQ5Nh0PQSaHIRBDWxqnqxmNcFsyNHjgAmeJVm8uTJAMydOxeAZ599ltdee41HH32Uo0ePEh4ezsyZM+nfv3+11CsiIiIiXsgvCJqPMIvDYYLY/i/Ncmyd87iMVEh+3ywAIV3NsP1Nh0HohZovrQbx2GCWH6yK69KlC7t27Sr364KDg5kyZQpTpkxxaX0iIiIiIgDYbNCgu1lipkH6Qdj/lQlph5ZBzinnsWmbzRL3gglljQeeuaI2DOp1NucSr+SxwUxERERExCMFNYWIm82SmwVHVkHKt3DwWzi2HnCY43Iz4OA3ZtkIBDUzg4iEDYEmQ6BWays/hbiYgpmIiIiIiFV8/SFsoFm6Pg0ZR8xVtIPfmkBW+Nm09ANFb3us3c4Z1MIG6/k0D6dgJiIiIiLiLgIbQeuxZnE44MROZ0g7vLLobY9/JJll9ztmvV4nZ1BrPBACyj8PsFhPwUxERERExB3ZbFAvyiwd7za3PR5bCynfw6Hv4cj/4Mzk1gAc32GW+H8CNqjf3dzyGDbEDCRir2PZR5GyKZiJiIiIiHgCX38I/T+zxDxqhuQ/8j8T0lK+N6HNkXvmYAf8vsEscTPA5gcNL3A+n9aor0Z8dDMKZiIiIiIinsgvCJoMNUtXIPsEpP5ogtqh780Q/fkcOSbEHfmfmT/NJwBC+zmfT2vQywQ/sYyCmYiIiIiIN7DXheaXmwXMQCKpPziD2omdzmPzMuHQcrMA+AZD4/4mpDUeDA16gI+iQnXSP20REREREW8U2AhaXWUWgNMHzoSx783Ij6d+dR6be9o5ND+YkBc6wAS1sMFQvyvYfKr/M9QgCmYiIiIiIjVBcDNoe71ZAP7YUyioLTfD8efLPgEHFpkFwL+BGekxbLC5/bFeJ0127WIKZiIiIiIiNVHttmYJn2iG5j+Z4Axph5ZD5mHnsVnHYN8CswAENobGg5zPqNVpr6B2nhTMRERERERqOpsN6nYwS/vbTVA7vsMZ1FJXQNbvzuMzUuG3T8wCENTMGdLCBpvAJxWiYCYiIiIiIkXZbBDS2SyRk8GRB79vdl5NS/0Bck46j08/AMnvmwWgVhtnSAsbDMEtLPkYnkTBTEREREREzs3mAw26myXqPsjLgWMbIHW5mUPt8E9mAJF8p5Ih6V2zgLnVMX/Ex7DBEBRmycdwZwpmIiIiIiJSMT5+0OgCs3SaArlZZoLrlO9NWDv8PzMkf76TCWZJnG3W63UyIa3JEDOoSEBDaz6HG1EwExERERGR8+PrD6H/ZxYehdwMOPKL8xm1o6shL9t5/PEdZkl4HbBBSBfnM2qNB4B/Pas+iWUUzERERERExLV8AyFskFkAck7B4Z+dz6gdWweO3DMHOyBts1l2vWxum6zf0/l8WuiFYK9t0QepPgpmIiIiIiJStfxqQdNhZgEzT1rqj8551H7fBDjMPkeeuS3y2FqIex5sftDwAmdQa9QP/IKs+iRVRsFMRERERESql70uNL/cLACZxyB1pTOoHd/mPNaRA0f+Z5btT4OPPzTqe+a2x4EmtPkFW/M5XEjBTERERERErBXQAFqONguYedJSf3AOJnJil/PYvCyzL/UHs27zgwY9oNGZZ9zq9Kru6l1CwUxERERERNxLYGNodY1ZAE4fODN/2pln1P5Ich7ryIGja8yy62WC/OoS3Ow1IMqS0itLwUxERERERNxbcDNoe71ZAE79CodWmAFFDv8EJ+IKDrXlnCAoM8GaOs+DgpmIiIiIiHiWWq2h3QSzAGQehSOr4PD/yHL4cyx3OI2trbDCFMxERERERMSzBTSE5iOg+QhyTp/GERdX9mvcjI/VBYiIiIiIiNR0CmYiIiIiIiIWUzATERERERGxmIKZiIiIiIiIxRTMRERERERELKZgJiIiIiIiYjEFMxEREREREYspmImIiIiIiFhMwUxERERERMRiCmYiIiIiIiIWUzATERERERGxmIKZiIiIiIiIxRTMRERERERELKZgJiIiIiIiYjEFMxEREREREYspmImIiIiIiFhMwUxERERERMRiNofD4bC6CG+yYcMGHA4H/v7+VpcCgMPhIDs7G7vdjs1ms7oc8QDqM1IR6i9SUeozUlHqM1JR7tRnsrKysNls9OjRo8xj/aqhnhrF6n/5xdlsNrcJieIZ1GekItRfpKLUZ6Si1Gekotypz9hstnLnA10xExERERERsZieMRMREREREbGYgpmIiIiIiIjFFMxEREREREQspmAmIiIiIiJiMQUzERERERERiymYiYiIiIiIWEzBTERERERExGIKZiIiIiIiIhZTMBMREREREbGYgpmIiIiIiIjFFMxEREREREQspmAmIiIiIiJiMQUzL5WXl8fMmTPp378/Xbt2ZeLEifz6669WlyUW2b9/P5GRkSWWTz/9FIC4uDjGjx9Pt27dGDRoEHPmzCnyevWnmuWNN97ghhtuKLLNFX2krHOI5yqtz0ydOrXEd86AAQMK9qvP1DxpaWlMmzaNAQMG0KNHD8aNG8e6desK9ut7Roorq8943feMQ7zSa6+95ujbt69jxYoVjri4OMfEiRMdF198sSMzM9Pq0sQCy5Ytc8TExDgOHTrkSE1NLVjS09Mdx44dc/Tu3dvx8MMPOxITEx3z5s1zxMTEOObNm1fwevWnmuPdd991REZGOsaPH1+wzRV9pDznEM9UWp9xOByOMWPGOF566aUi3zlHjx4t2K8+U/PcdNNNjiuuuMKxdu1ax+7dux3Tp093dOnSxZGYmKjvGSnVufqMw+F93zMKZl4oMzPT0b17d8cHH3xQsO348eOOLl26OBYtWmRhZWKVN99803HFFVeUum/WrFmO/v37O7Kzswu2vfjii45LLrnE4XCoP9UUKSkpjkmTJjm6devmGD58eJFfsl3RR8o6h3iec/WZnJwcR0xMjOO7774r9bXqMzVPcnKyo0OHDo7169cXbMvLy3NcfPHFjldeeUXfM1JCWX3GG79ndCujF9q5cyenTp2iT58+Bdvq1q1Lp06dWLt2rYWViVV27dpFREREqfvWrVtHr1698PPzK9jWp08f9uzZw9GjR9Wfaojt27dTr149vvjiC7p27Vpknyv6SFnnEM9zrj6TnJxMZmYm4eHhpb5WfabmqV+/PrNnzyY6Orpgm81mw+FwcPz4cX3PSAll9Rlv/J5RMPNCKSkpADRt2rTI9saNG3Pw4EErShKLxcfHc/ToUf70pz/Rr18/xo0bx48//giY/tKkSZMixzdu3BiAAwcOqD/VEEOGDOHFF1+kZcuWJfa5oo+UdQ7xPOfqM/Hx8dhsNt577z2GDBnCRRddxPTp0zl58iRQvv9Pqc94l7p16zJw4ED8/f0Lti1ZsoTffvuNCy+8UN8zUkJZfcYbv2cUzLxQeno6QJGODBAQEEBmZqYVJYmFsrKySE5O5o8//uCee+5h9uzZxMTEcMstt7Bq1SoyMjJK7SsAmZmZ6k/ikj5S1jnEuyQkJODj40Pz5s2ZNWsWU6ZM4YcffuCOO+4gLy9PfUZYv349Dz30EEOHDmXIkCH6npEyFe8z3vg941f2IeJpAgMDAfMLeX4bTAcLCgqyqiyxiL+/P2vXrsXPz6/gyyc6Oprdu3czZ84cAgMDycrKKvKa/C+j4OBg9SdxSR8p6xziXSZPnsyNN95I3bp1AejQoQOhoaGMHTuWrVu3qs/UcEuXLuX++++na9euvPTSS4C+Z+TcSusz3vg9oytmXij/km1qamqR7ampqSUu10rNEBwcXOIvQh06dODQoUM0adKk1L4CEBYWpv4kLukjZZ1DvIvNZiv4ZSlfhw4dAHPrkPpMzfX+++8zefJkBgwYwNtvv13wC7O+Z+RsztZnvPF7RsHMC3Xs2JHatWuzevXqgm0nTpxgx44dxMbGWliZWGHnzp107969yLwfANu2bSMiIoJevXqxfv16cnNzC/atWrWKtm3b0rBhQ/UncUkfKesc4l3+9re/MWnSpCLbtm7dCkBERIT6TA31wQcfMH36dK6//npeeeWVIn8w1PeMlOZcfcYrv2csGQtSqtxLL73kuOCCCxxLly4tmLdh2LBhmneqBsrNzXVcc801jhEjRjjWrl3rSExMdDzzzDOO6Ohox86dOx1Hjhxx9OrVyzFlyhRHQkKC47PPPnPExMQ45s+fX3AO9aeaZcqUKUWGPndFHynPOcRzFe8z33//vSMyMtLxxhtvOH799VfHihUrHEOGDHHcd999Bceoz9QsSUlJjs6dOzvuvPPOInNOpaamOk6cOKHvGSmhrD7jjd8zNofD4aj+OChVLTc3l5deeon58+eTkZFBr169mDZtGi1atLC6NLHAsWPHmDFjBitXruTEiRN06tSJ+++/v+AvRlu2bOHpp59mx44dhIaGMnHiRMaPH1/wevWnmuXBBx9k//79zJ07t2CbK/pIWecQz1Van/nmm2+YNWsWSUlJ1KlTh5EjR3LPPfcUPFivPlOzzJo1i5dffrnUfWPGjOHZZ5/V94wUUZ4+423fMwpmIiIiIiIiFtMzZiIiIiIiIhZTMBMREREREbGYgpmIiIiIiIjFFMxEREREREQspmAmIiIiIiJiMQUzERERERERiymYiYiIiIiIWEzBTERE5CwyMzNJSUkBYP78+URGRjJ//nyLqxIREW+kYCYiIlKKHTt2cOmll/K///0PgF69evH888/Tq1cviysTERFv5Gd1ASIiIu5o586d7N+/v2C9ZcuWtGzZ0sKKRETEm+mKmYiIiIiIiMUUzERERIp58MEHmTp1KgBTp04teLas+DNmkZGRPPnkkyxatIhRo0YRExPDwIEDeeONN3A4HHz99deMHj2aLl26cPHFFzNnzpwS73Xo0CEeffRRBgwYQHR0NIMHD+app57i999/r7bPKyIi1tOtjCIiIsWMHTsWf39/Pv74Y8aOHUvPnj3Jzc0t9djly5ezaNEixo8fz7XXXstHH33Eq6++ypYtW9iwYQPjx49n7NixfPjhhzz//PM0bdqUyy67DIC9e/cybtw4srKyGDt2LM2bN2fnzp189NFHrFy5ko8++ogGDRpU50cXERGLKJiJiIgU0717d/bs2cPHH39Mt27dGDVq1FlHYzxw4ACffvopXbp0AaBnz56MGjWKFStW8OmnnxITEwPABRdcwGWXXcby5csLgtn06dNJT09nwYIFtGrVquCcw4YN46abbmLmzJk8/vjjVfthRUTELehWRhERkfPQsmXLglAGEB4eDkDr1q0LQln+OphbFwFOnDjBjz/+SGxsLLVr1+bYsWMFS8eOHWnZsiXfffddNX4SERGxkq6YiYiInIdGjRoVWbfb7QCEhoYW2e7nZ/6X63A4AEhOTiYvL48VK1bQt2/fs54/IyODwMBAV5YsIiJuSMFMRETkPOQHseJsNts5X5eXlwfAJZdcwnXXXXfW4/IDnYiIeDd924uIiFigRYsWAGRmZtKvX78S+5cuXUpISIiCmYhIDaFnzERERErh42P+F5l/ZcvVGjVqRM+ePVm5ciXr168vsm/lypXceeedzJ49u0reW0RE3I/+DCciIlKK/GfHvvjiCxwOR8GzYa702GOPMX78eG688UbGjh1L+/btSUpK4qOPPiIkJIQpU6a4/D1FRMQ96YqZiIhIKfr06cPIkSPZsmULzzzzTJUEs/wJq0eMGMHXX3/N9OnT+fbbbxk+fDiffPJJwQiPIiLi/WyOqvg/jYiIiIiIiJSbrpiJiIiIiIhYTMFMRERERETEYgpmIiIiIiIiFlMwExERERERsZiCmYiIiIiIiMUUzERERERERCymYCYiIiIiImIxBTMRERERERGLKZiJiIiIiIhYTMFMRERERETEYgpmIiIiIiIiFlMwExERERERsZiCmYiIiIiIiMX+H09PuOyScS4FAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# 使用 seaborn 设置美化参数\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# 创建一个新的图形\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "ind = 4000\n",
    "max_step = 2500\n",
    "x = [i for i in range(ind)]\n",
    "# 创建一个示例数据\n",
    "y0 = [math.log(item) for item in train_loss_NSA_plus[0:ind]]\n",
    "k0 = 6.4\n",
    "x0 = [item*k0 for item in x if item*k0 < max_step]\n",
    "y0 = y0[0:len(x0)]\n",
    "y1 = [math.log(item) for item in train_loss_NSA[0:ind]]\n",
    "k1 = 4.6\n",
    "x1 = [item*k1 for item in x if item*k1 < max_step]\n",
    "y1 = y1[0:len(x1)]\n",
    "y2 = [math.log(item) for item in train_loss_GD[0:ind]]\n",
    "k2 = 1.2\n",
    "x2 = [item*k2 for item in x if item*k2 < max_step]\n",
    "y2 = y2[0:len(x2)]\n",
    "y3 = [math.log(item) for item in train_loss_Adam[0:ind]]\n",
    "k3 = 1.9\n",
    "x3 = [item*k3 for item in x if item*k3 < max_step]\n",
    "y3 = y3[0:len(x3)]\n",
    "y4 = [math.log(item) for item in train_loss_FISTA[0:ind]]\n",
    "k4 = 4.9\n",
    "x4 = [item*k4 for item in x if item*k4 < max_step]\n",
    "y4 = y4[0:len(x4)]\n",
    "\n",
    "# 绘制折线图\n",
    "plt.plot(x0, y0, color='orange', linewidth=2, linestyle='-',label=\"NSA_plus\")\n",
    "plt.plot(x1, y1, color='blue', linewidth=2, linestyle='-', label=\"NSA\")\n",
    "plt.plot(x2, y2, color='red', linewidth=2, linestyle='-', label=\"GD\")\n",
    "plt.plot(x3, y3, color='green', linewidth=2, linestyle='-', label=\"Adam\")\n",
    "plt.plot(x4, y4, color='black', linewidth=2, linestyle='-', label=\"FISTA\")\n",
    "#plt.plot(x, y0, color='yellow', linewidth=2, linestyle='-', marker='o', markersize=5, markerfacecolor='yellow', markeredgecolor='yellow',label=\"NSA_plus\")\n",
    "#plt.plot(x, y1, color='blue', linewidth=2, linestyle='-', marker='o', markersize=5, markerfacecolor='blue', markeredgecolor='blue',label=\"NSA\")\n",
    "#plt.plot(x, y2, color='red', linewidth=2, linestyle='-', marker='o', markersize=5, markerfacecolor='red', markeredgecolor='red',label=\"GD\")\n",
    "#plt.plot(x, y3, color='green', linewidth=2, linestyle='-', marker='o', markersize=5, markerfacecolor='green', markeredgecolor='green',label=\"Adam\")\n",
    "#plt.plot(x, y4, color='black', linewidth=2, linestyle='-', marker='o', markersize=5, markerfacecolor='black', markeredgecolor='black',label=\"FISTA\")\n",
    "# 添加标题和标签\n",
    "plt.title('Loss Function', fontsize=20)\n",
    "plt.xlabel('time', fontsize=14)\n",
    "plt.ylabel('Y', fontsize=14)\n",
    "\n",
    "# 显示网格\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "# 显示图形\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5efdfc00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "测试集模型准确率: 100.00%\n",
      "\n",
      "训练集模型准确率: 100.00%\n"
     ]
    }
   ],
   "source": [
    "predictions_test = nn_NSA_plus.predict(X_test)\n",
    "predicted_classes_test = np.argmax(predictions_test, axis=1)\n",
    "true_classes_test = np.argmax(y_test, axis=1)\n",
    "print(f\"\\n测试集模型准确率: {np.mean(predicted_classes_test == true_classes_test) * 100:.2f}%\")\n",
    "\n",
    "predicteds_train = nn_NSA_plus.predict(X_train)\n",
    "predicted_classes_train = np.argmax(predicteds_train, axis=1)\n",
    "true_classes_train = np.argmax(y_train,axis=1)\n",
    "print(f\"\\n训练集模型准确率: {np.mean(predicted_classes_train == true_classes_train) * 100:.2f}%\")\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
